2-Postprocess-kidneys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1da4f26a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['E:/AAV para enfermedades renales/CT/Tanda 5 (2021-2022)\\\\01 - 26NOV21-PCKD+1control\\\\TROPISMORENAL_RATON2_1D_26NOV21_26Nov2021_Acq001_ring', 'E:/AAV para enfermedades renales/CT/Tanda 5 (2021-2022)\\\\01 - 26NOV21-PCKD+1control\\\\TROPISMORENAL_RATON3_1I_26NOV21_26Nov2021_Acq001_ring']\n"
     ]
    }
   ],
   "source": [
    "#Run this first cell to define all main paths. Then either jump to the postprocessing windows near\n",
    "#the end or run the unet segmentation cells (just after this cell) in case you need it.\n",
    "\n",
    "#Set this to true only if you want to obtain cyst segmentations from manually corrected kidney masks,\n",
    "#not running unet segmentation, but just the postprocessing part. Set it to false to do the whole \n",
    "#processing from the original Unet masks\n",
    "manually_corr_kidneysMask = True\n",
    "\n",
    "\n",
    "from glob import glob as glob\n",
    "\n",
    "#Folder with the original image as an image sequence. It can be changed to your folder of choice\n",
    "Data_folders = glob('E:/AAV para enfermedades renales/CT/Tanda 5 (2021-2022)/*/*')\n",
    "#Data_folders = glob('E:/AAV para enfermedades renales/CT/Tanda 7 (2022-2023)/*/*')\n",
    "\n",
    "\n",
    "\n",
    "#Data_folders = 'D:/AAV para enfermedades renales/Imágenes de CT y MR/CT/Pathologic/4 - 10DIC21-PCKD+1control/'+\\\n",
    "#'TROPISMORENAL_RATON5_2I_10DIC21_10Dec2021_Acq001_ring/TROPISMORENAL_RATON5_2I_10DIC21_10Dec2021_Acq001_ring'\n",
    "\n",
    "#Data_folder = 'D:/AAV para enfermedades renales/Imágenes de CT y MR/Unet2D (CT)/'+\\\n",
    "#'inference/TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring-Results'\n",
    "\n",
    "\n",
    "#Folder where the segmented masks of 2D images obtained with Unet will be saved. Also a \n",
    "#.tif stack with the same name as the folder will be created, with the mask after binarization\n",
    "#and connected components analysis.\n",
    "\n",
    "#Define the general results folder. \n",
    "Results_folders = 'E:/AAV para enfermedades renales/Imagenes de CT y MR/Unet2D (CT)/inference'\n",
    "#Results_folders = 'E:/AAV para enfermedades renales/Imagenes de CT y MR/Unet2D (CT)/inference'+\\\n",
    "#'/0-Alternative_cyst_segmentationTanda7'\n",
    "\n",
    "#Path to the folder with the model to be used on the Unet\n",
    "Prediction_model_folder = 'D:/AAV para enfermedades renales/Imágenes de CT y MR/Unet2D (CT)/models/'+\\\n",
    "'cysts4-Imagenet-pretrained(fromEpoch134)'\n",
    "\n",
    "#Threshold to binarize the initial segmentation.\n",
    "threshold = 112\n",
    "\n",
    "#Radius for the circular kernel of the closing operation after connected components\n",
    "radiusClosing = 4\n",
    "\n",
    "#Radius to slightly erode the borders of the kidney mask, too extended\n",
    "\n",
    "radiusErosion = 3\n",
    "#radiusErosion = 0\n",
    "\n",
    "#If you are extracting cysts from manually corrected kidney masks, the kidney masks should be in the\n",
    "#same folder as the corresponding image. The output image for the cyst masks will also be there.\n",
    "\n",
    "    \n",
    "\n",
    "slice1 = 252\n",
    "\n",
    "import os\n",
    "import tifffile as tiff\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cc3d\n",
    "import cv2\n",
    "import scipy\n",
    "from scipy import ndimage\n",
    "#import skimage\n",
    "import skimage.feature as feature\n",
    "import skimage.segmentation as segmentation\n",
    "#Voxel size for the images to be analyzed [Z,X,Y]\n",
    "voxelSize = np.array([0.122,0.122,0.122])\n",
    "print(Data_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd76bfcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n",
      "-------------------\n",
      "U-Net and dependencies installed.\n",
      "Notebook version: 1.13\n",
      "Latest notebook version: 1.13\n",
      "This notebook is up-to-date.\n"
     ]
    }
   ],
   "source": [
    "#In case you want to run the unet,\n",
    "#Load Unet2 dependencies. Otherwise don't run\n",
    "\n",
    "Notebook_version = '1.13'\n",
    "Network = 'U-Net (2D)'\n",
    "\n",
    "\n",
    "from builtins import any as b_any\n",
    "\n",
    "def get_requirements_path():\n",
    "    # Store requirements file in 'contents' directory \n",
    "    current_dir = os.getcwd()\n",
    "    dir_count = current_dir.count('/') - 1\n",
    "    path = '../' * (dir_count) + 'requirements.txt'\n",
    "    return path\n",
    "\n",
    "def filter_files(file_list, filter_list):\n",
    "    filtered_list = []\n",
    "    for fname in file_list:\n",
    "        if b_any(fname.split('==')[0] in s for s in filter_list):\n",
    "            filtered_list.append(fname)\n",
    "    return filtered_list\n",
    "\n",
    "def build_requirements_file(before, after):\n",
    "    path = get_requirements_path()\n",
    "\n",
    "    # Exporting requirements.txt for local run\n",
    "    !pip freeze > $path\n",
    "\n",
    "    # Get minimum requirements file\n",
    "    df = pd.read_csv(path, delimiter = \"\\n\")\n",
    "    mod_list = [m.split('.')[0] for m in after if not m in before]\n",
    "    req_list_temp = df.values.tolist()\n",
    "    req_list = [x[0] for x in req_list_temp]\n",
    "\n",
    "    # Replace with package name and handle cases where import name is different to module name\n",
    "    mod_name_list = [['sklearn', 'scikit-learn'], ['skimage', 'scikit-image']]\n",
    "    mod_replace_list = [[x[1] for x in mod_name_list] if s in [x[0] for x in mod_name_list] else s for s in mod_list] \n",
    "    filtered_list = filter_files(req_list, mod_replace_list)\n",
    "\n",
    "    file=open(path,'w')\n",
    "    for item in filtered_list:\n",
    "        file.writelines(item + '\\n')\n",
    "\n",
    "    file.close()\n",
    "\n",
    "import sys\n",
    "before = [str(m) for m in sys.modules]\n",
    "\n",
    "#@markdown ##Load key U-Net dependencies\n",
    "\n",
    "#As this notebokk depends mostly on keras which runs a tensorflow backend (which in turn is pre-installed in colab)\n",
    "#only the data library needs to be additionally installed.\n",
    "#%tensorflow_version 1.x\n",
    "import tensorflow as tf\n",
    "# print(tensorflow.__version__)\n",
    "# print(\"Tensorflow enabled.\")\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "import segmentation_models\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    "# Keras imports\n",
    "from keras import models\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Conv2D, MaxPooling2D, Dropout, concatenate, UpSampling2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "# from keras.callbacks import ModelCheckpoint, LearningRateScheduler, CSVLogger # we currently don't use any other callbacks from ModelCheckpoints\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
    "from keras import backend as keras\n",
    "\n",
    "# General import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from skimage import img_as_ubyte, io, transform\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imread\n",
    "from pathlib import Path\n",
    "import shutil\n",
    "import random\n",
    "import time\n",
    "import csv\n",
    "import sys\n",
    "from math import ceil\n",
    "from fpdf import FPDF, HTMLMixin\n",
    "from pip._internal.operations.freeze import freeze\n",
    "import subprocess\n",
    "# Imports for QC\n",
    "from PIL import Image\n",
    "from scipy import signal\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from skimage.util import img_as_uint\n",
    "from skimage.metrics import structural_similarity\n",
    "from skimage.metrics import peak_signal_noise_ratio as psnr\n",
    "\n",
    "# For sliders and dropdown menu and progress bar\n",
    "from ipywidgets import interact\n",
    "import ipywidgets as widgets\n",
    "# from tqdm import tqdm\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "from sklearn.feature_extraction import image\n",
    "from skimage import img_as_ubyte, io, transform\n",
    "from skimage.util.shape import view_as_windows\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "# Suppressing some warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def create_patches(Training_source, Training_target, patch_width, patch_height, min_fraction):\n",
    "  \"\"\"\n",
    "  Function creates patches from the Training_source and Training_target images. \n",
    "  The steps parameter indicates the offset between patches and, if integer, is the same in x and y.\n",
    "  Saves all created patches in two new directories in the /content folder.\n",
    "\n",
    "  min_fraction is the minimum fraction of pixels that need to be foreground to be considered as a valid patch\n",
    "\n",
    "  Returns: - Two paths to where the patches are now saved\n",
    "  \"\"\"\n",
    "  DEBUG = False\n",
    "\n",
    "  Patch_source = os.path.join('/content','img_patches')\n",
    "  Patch_target = os.path.join('/content','mask_patches')\n",
    "  Patch_rejected = os.path.join('/content','rejected')\n",
    "  \n",
    "\n",
    "  #Here we save the patches, in the /content directory as they will not usually be needed after training\n",
    "  if os.path.exists(Patch_source):\n",
    "    shutil.rmtree(Patch_source)\n",
    "  if os.path.exists(Patch_target):\n",
    "    shutil.rmtree(Patch_target)\n",
    "  if os.path.exists(Patch_rejected):\n",
    "    shutil.rmtree(Patch_rejected)\n",
    "\n",
    "  os.mkdir(Patch_source)\n",
    "  os.mkdir(Patch_target)\n",
    "  os.mkdir(Patch_rejected) #This directory will contain the images that have too little signal.\n",
    "  \n",
    "  patch_num = 0\n",
    "\n",
    "  for file in tqdm(os.listdir(Training_source)):\n",
    "\n",
    "    img = io.imread(os.path.join(Training_source, file))\n",
    "    mask = io.imread(os.path.join(Training_target, file),as_gray=True)\n",
    "\n",
    "    if DEBUG:\n",
    "      print(file)\n",
    "      print(img.dtype)\n",
    "\n",
    "    # Using view_as_windows with step size equal to the patch size to ensure there is no overlap\n",
    "    patches_img = view_as_windows(img, (patch_width, patch_height), (patch_width, patch_height))\n",
    "    patches_mask = view_as_windows(mask, (patch_width, patch_height), (patch_width, patch_height))\n",
    "\n",
    "    patches_img = patches_img.reshape(patches_img.shape[0]*patches_img.shape[1], patch_width,patch_height)\n",
    "    patches_mask = patches_mask.reshape(patches_mask.shape[0]*patches_mask.shape[1], patch_width,patch_height)\n",
    "\n",
    "    if DEBUG:\n",
    "      print(all_patches_img.shape)\n",
    "      print(all_patches_img.dtype)\n",
    "\n",
    "    for i in range(patches_img.shape[0]):\n",
    "      img_save_path = os.path.join(Patch_source,'patch_'+str(patch_num)+'.tif')\n",
    "      mask_save_path = os.path.join(Patch_target,'patch_'+str(patch_num)+'.tif')\n",
    "      patch_num += 1\n",
    "\n",
    "      # if the mask conatins at least 2% of its total number pixels as mask, then go ahead and save the images\n",
    "      pixel_threshold_array = sorted(patches_mask[i].flatten())\n",
    "      if pixel_threshold_array[int(round(len(pixel_threshold_array)*(1-min_fraction)))]>0:\n",
    "        io.imsave(img_save_path, img_as_ubyte(normalizeMinMax(patches_img[i])))\n",
    "        io.imsave(mask_save_path, convert2Mask(normalizeMinMax(patches_mask[i]),0))\n",
    "      else:\n",
    "        io.imsave(Patch_rejected+'/patch_'+str(patch_num)+'_image.tif', img_as_ubyte(normalizeMinMax(patches_img[i])))\n",
    "        io.imsave(Patch_rejected+'/patch_'+str(patch_num)+'_mask.tif', convert2Mask(normalizeMinMax(patches_mask[i]),0))\n",
    "\n",
    "  return Patch_source, Patch_target\n",
    "\n",
    "\n",
    "def estimatePatchSize(data_path, max_width = 512, max_height = 512):\n",
    "\n",
    "  files = os.listdir(data_path)\n",
    "  \n",
    "  # Get the size of the first image found in the folder and initialise the variables to that\n",
    "  n = 0 \n",
    "  while os.path.isdir(os.path.join(data_path, files[n])):\n",
    "    n += 1\n",
    "  (height_min, width_min) = Image.open(os.path.join(data_path, files[n])).size\n",
    "\n",
    "  # Screen the size of all dataset to find the minimum image size\n",
    "  for file in files:\n",
    "    if not os.path.isdir(os.path.join(data_path, file)):\n",
    "      (height, width) = Image.open(os.path.join(data_path, file)).size\n",
    "      if width < width_min:\n",
    "        width_min = width\n",
    "      if height < height_min:\n",
    "        height_min = height\n",
    "  \n",
    "  # Find the power of patches that will fit within the smallest dataset\n",
    "  width_min, height_min = (fittingPowerOfTwo(width_min), fittingPowerOfTwo(height_min))\n",
    "\n",
    "  # Clip values at maximum permissible values\n",
    "  if width_min > max_width:\n",
    "    width_min = max_width\n",
    "\n",
    "  if height_min > max_height:\n",
    "    height_min = max_height\n",
    "  \n",
    "  return (width_min, height_min)\n",
    "\n",
    "def fittingPowerOfTwo(number):\n",
    "  n = 0\n",
    "  while 2**n <= number:\n",
    "    n += 1 \n",
    "  return 2**(n-1)\n",
    "\n",
    "\n",
    "def getClassWeights(Training_target_path):\n",
    "\n",
    "  Mask_dir_list = os.listdir(Training_target_path)\n",
    "  number_of_dataset = len(Mask_dir_list)\n",
    "\n",
    "  class_count = np.zeros(2, dtype=int)\n",
    "  for i in tqdm(range(number_of_dataset)):\n",
    "    print(Mask_dir_list[i])\n",
    "    mask = io.imread(os.path.join(Training_target_path, Mask_dir_list[i]))\n",
    "    mask = normalizeMinMax(mask)\n",
    "    class_count[0] += mask.shape[0]*mask.shape[1] - mask.sum()\n",
    "    class_count[1] += mask.sum()\n",
    "\n",
    "  n_samples = class_count.sum()\n",
    "  n_classes = 2\n",
    "\n",
    "  class_weights = n_samples / (n_classes * class_count)\n",
    "  return class_weights\n",
    "\n",
    "def weighted_binary_crossentropy(class_weights):\n",
    "\n",
    "    def _weighted_binary_crossentropy(y_true, y_pred):\n",
    "        binary_crossentropy = keras.binary_crossentropy(y_true, y_pred)\n",
    "        weight_vector = y_true * class_weights[1] + (1. - y_true) * class_weights[0]\n",
    "        weighted_binary_crossentropy = weight_vector * binary_crossentropy\n",
    "\n",
    "        return keras.mean(weighted_binary_crossentropy)\n",
    "\n",
    "    return _weighted_binary_crossentropy\n",
    "\n",
    "\n",
    "def save_augment(datagen,orig_img,dir_augmented_data=\"/content/augment\"):\n",
    "  \"\"\"\n",
    "  Saves a subset of the augmented data for visualisation, by default in /content.\n",
    "\n",
    "  This is adapted from: https://fairyonice.github.io/Learn-about-ImageDataGenerator.html\n",
    "  \n",
    "  \"\"\"\n",
    "  try:\n",
    "    os.mkdir(dir_augmented_data)\n",
    "  except:\n",
    "        ## if the preview folder exists, then remove\n",
    "        ## the contents (pictures) in the folder\n",
    "    for item in os.listdir(dir_augmented_data):\n",
    "      os.remove(dir_augmented_data + \"/\" + item)\n",
    "\n",
    "    ## convert the original image to array\n",
    "  x = img_to_array(orig_img)\n",
    "    ## reshape (Sampke, Nrow, Ncol, 3) 3 = R, G or B\n",
    "    #print(x.shape)\n",
    "  x = x.reshape((1,) + x.shape)\n",
    "    #print(x.shape)\n",
    "    ## -------------------------- ##\n",
    "    ## randomly generate pictures\n",
    "    ## -------------------------- ##\n",
    "  i = 0\n",
    "    #We will just save 5 images,\n",
    "    #but this can be changed, but note the visualisation in 3. currently uses 5.\n",
    "  Nplot = 5\n",
    "  for batch in datagen.flow(x,batch_size=1,\n",
    "                            save_to_dir=dir_augmented_data,\n",
    "                            save_format='tif',\n",
    "                            seed=42):\n",
    "    i += 1\n",
    "    if i > Nplot - 1:\n",
    "      break\n",
    "\n",
    "# Generators\n",
    "def buildDoubleGenerator(image_datagen, mask_datagen, image_folder_path, mask_folder_path, subset, batch_size, target_size):\n",
    "  '''\n",
    "  Can generate image and mask at the same time use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same\n",
    "  \n",
    "  datagen: ImageDataGenerator \n",
    "  subset: can take either 'training' or 'validation'\n",
    "  '''\n",
    "  seed = 1\n",
    "  image_generator = image_datagen.flow_from_directory(\n",
    "      os.path.dirname(image_folder_path),\n",
    "      classes = [os.path.basename(image_folder_path)],\n",
    "      class_mode = None,\n",
    "      color_mode = \"grayscale\",\n",
    "      target_size = target_size,\n",
    "      batch_size = batch_size,\n",
    "      subset = subset,\n",
    "      interpolation = \"bicubic\",\n",
    "      seed = seed)\n",
    "  \n",
    "  mask_generator = mask_datagen.flow_from_directory(\n",
    "      os.path.dirname(mask_folder_path),\n",
    "      classes = [os.path.basename(mask_folder_path)],\n",
    "      class_mode = None,\n",
    "      color_mode = \"grayscale\",\n",
    "      target_size = target_size,\n",
    "      batch_size = batch_size,\n",
    "      subset = subset,\n",
    "      interpolation = \"nearest\",\n",
    "      seed = seed)\n",
    "  \n",
    "  this_generator = zip(image_generator, mask_generator)\n",
    "  for (img,mask) in this_generator:\n",
    "      # img,mask = adjustData(img,mask)\n",
    "      yield (img,mask)\n",
    "\n",
    "\n",
    "def prepareGenerators(image_folder_path, mask_folder_path, datagen_parameters, batch_size = 4, target_size = (512, 512)):\n",
    "  image_datagen = ImageDataGenerator(**datagen_parameters, preprocessing_function = normalizePercentile)\n",
    "  mask_datagen = ImageDataGenerator(**datagen_parameters, preprocessing_function = normalizeMinMax)\n",
    "\n",
    "  train_datagen = buildDoubleGenerator(image_datagen, mask_datagen, image_folder_path, mask_folder_path, 'training', batch_size, target_size)\n",
    "  validation_datagen = buildDoubleGenerator(image_datagen, mask_datagen, image_folder_path, mask_folder_path, 'validation', batch_size, target_size)\n",
    "\n",
    "  return (train_datagen, validation_datagen)\n",
    "\n",
    "\n",
    "# Normalization functions from Martin Weigert\n",
    "def normalizePercentile(x, pmin=1, pmax=99.8, axis=None, clip=False, eps=1e-20, dtype=np.float32):\n",
    "    \"\"\"This function is adapted from Martin Weigert\"\"\"\n",
    "    \"\"\"Percentile-based image normalization.\"\"\"\n",
    "\n",
    "    mi = np.percentile(x,pmin,axis=axis,keepdims=True)\n",
    "    ma = np.percentile(x,pmax,axis=axis,keepdims=True)\n",
    "    return normalize_mi_ma(x, mi, ma, clip=clip, eps=eps, dtype=dtype)\n",
    "\n",
    "\n",
    "def normalize_mi_ma(x, mi, ma, clip=False, eps=1e-20, dtype=np.float32):#dtype=np.float32\n",
    "    \"\"\"This function is adapted from Martin Weigert\"\"\"\n",
    "    if dtype is not None:\n",
    "        x   = x.astype(dtype,copy=False)\n",
    "        mi  = dtype(mi) if np.isscalar(mi) else mi.astype(dtype,copy=False)\n",
    "        ma  = dtype(ma) if np.isscalar(ma) else ma.astype(dtype,copy=False)\n",
    "        eps = dtype(eps)\n",
    "\n",
    "    try:\n",
    "        import numexpr\n",
    "        x = numexpr.evaluate(\"(x - mi) / ( ma - mi + eps )\")\n",
    "    except ImportError:\n",
    "        x =                   (x - mi) / ( ma - mi + eps )\n",
    "\n",
    "    if clip:\n",
    "        x = np.clip(x,0,1)\n",
    "\n",
    "    return x\n",
    "\n",
    "\n",
    "\n",
    "# Simple normalization to min/max fir the Mask\n",
    "def normalizeMinMax(x, dtype=np.float32):\n",
    "  x = x.astype(dtype,copy=False)\n",
    "  x = (x - np.amin(x)) / (np.amax(x) - np.amin(x))\n",
    "  return x\n",
    "\n",
    "\n",
    "# This is code outlines the architecture of U-net. The choice of pooling steps decides the depth of the network. \n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "def unet(pretrained_weights = None, input_size = (256,256,1), pooling_steps = 4, learning_rate = 1e-4, verbose=True, class_weights=np.ones(2)):\n",
    "    \n",
    "    print('----Unet Definition----')\n",
    "\n",
    "    base_model = segmentation_models.Unet('resnet34',encoder_weights='imagenet')\n",
    "\n",
    "    print('base model.summary()')\n",
    "    base_model.summary()\n",
    "\n",
    "    inp = Input(shape=input_size)\n",
    "    print('inp: ',inp)\n",
    "    l1 = Conv2D(3, (1, 1))(inp) # map N channels data to 3 channels\n",
    "    print('l1: ',l1)\n",
    "    out = base_model(l1)\n",
    "    print('out: ',out)\n",
    "    model = Model(inp, out, name=base_model.name)\n",
    "\n",
    "    # model.compile(optimizer = Adam(lr = learning_rate), loss = 'binary_crossentropy', metrics = ['acc'])\n",
    "    model.compile(optimizer = Adam(lr = learning_rate), loss = weighted_binary_crossentropy(class_weights))\n",
    "\n",
    "\n",
    "    if verbose:\n",
    "      model.summary()\n",
    "\n",
    "    if(pretrained_weights):\n",
    "    \tmodel.load_weights(pretrained_weights);\n",
    "\n",
    "    return model\n",
    "\n",
    "#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
    "\n",
    "def predict_as_tiles(Image_path, model):\n",
    "\n",
    "  # Read the data in and normalize\n",
    "  Image_raw = io.imread(Image_path, as_gray = True)\n",
    "  Image_raw = normalizePercentile(Image_raw)\n",
    "\n",
    "  # Get the patch size from the input layer of the model\n",
    "  patch_size = model.layers[0].output_shape[0][1:3]\n",
    "\n",
    "  # Pad the image with zeros if any of its dimensions is smaller than the patch size\n",
    "  if Image_raw.shape[0] < patch_size[0] or Image_raw.shape[1] < patch_size[1]:\n",
    "    Image = np.zeros((max(Image_raw.shape[0], patch_size[0]), max(Image_raw.shape[1], patch_size[1])))\n",
    "    Image[0:Image_raw.shape[0], 0: Image_raw.shape[1]] = Image_raw\n",
    "  else:\n",
    "    Image = Image_raw\n",
    "\n",
    "  # Calculate the number of patches in each dimension\n",
    "  n_patch_in_width = ceil(Image.shape[0]/patch_size[0])\n",
    "  n_patch_in_height = ceil(Image.shape[1]/patch_size[1])\n",
    "\n",
    "  prediction = np.zeros(Image.shape)\n",
    "\n",
    "  for x in range(n_patch_in_width):\n",
    "    for y in range(n_patch_in_height):\n",
    "      xi = patch_size[0]*x\n",
    "      yi = patch_size[1]*y\n",
    "\n",
    "      # If the patch exceeds the edge of the image shift it back \n",
    "      if xi+patch_size[0] >= Image.shape[0]:\n",
    "        xi = Image.shape[0]-patch_size[0]\n",
    "\n",
    "      if yi+patch_size[1] >= Image.shape[1]:\n",
    "        yi = Image.shape[1]-patch_size[1]\n",
    "      \n",
    "      # Extract and reshape the patch\n",
    "      patch = Image[xi:xi+patch_size[0], yi:yi+patch_size[1]]\n",
    "      patch = np.reshape(patch,patch.shape+(1,))\n",
    "      patch = np.reshape(patch,(1,)+patch.shape)\n",
    "\n",
    "      # Get the prediction from the patch and paste it in the prediction in the right place\n",
    "      predicted_patch = model.predict(patch, batch_size = 1)\n",
    "      prediction[xi:xi+patch_size[0], yi:yi+patch_size[1]] = np.squeeze(predicted_patch)\n",
    "\n",
    "\n",
    "  return prediction[0:Image_raw.shape[0], 0: Image_raw.shape[1]]\n",
    "  \n",
    "\n",
    "\n",
    "\n",
    "def saveResult(save_path, nparray, source_dir_list, prefix='', threshold=None):\n",
    "  for (filename, image) in zip(source_dir_list, nparray):\n",
    "      io.imsave(os.path.join(save_path, prefix+os.path.splitext(filename)[0]+'.tif'), img_as_ubyte(image)) # saving as unsigned 8-bit image\n",
    "      \n",
    "      # For masks, threshold the images and return 8 bit image\n",
    "      if threshold is not None:\n",
    "        mask = convert2Mask(image, threshold)\n",
    "        io.imsave(os.path.join(save_path, prefix+'mask_'+os.path.splitext(filename)[0]+'.tif'), mask)\n",
    "\n",
    "\n",
    "def convert2Mask(image, threshold):\n",
    "  mask = img_as_ubyte(image, force_copy=True)\n",
    "  mask[mask > threshold] = 255\n",
    "  mask[mask <= threshold] = 0\n",
    "  return mask\n",
    "\n",
    "\n",
    "def getIoUvsThreshold(prediction_filepath, groud_truth_filepath):\n",
    "  prediction = io.imread(prediction_filepath)\n",
    "  ground_truth_image = img_as_ubyte(io.imread(groud_truth_filepath, as_gray=True), force_copy=True)\n",
    "\n",
    "  threshold_list = []\n",
    "  IoU_scores_list = []\n",
    "\n",
    "  for threshold in range(0,256): \n",
    "    # Convert to 8-bit for calculating the IoU\n",
    "    mask = img_as_ubyte(prediction, force_copy=True)\n",
    "    mask[mask > threshold] = 255\n",
    "    mask[mask <= threshold] = 0\n",
    "\n",
    "    # Intersection over Union metric\n",
    "    intersection = np.logical_and(ground_truth_image, np.squeeze(mask))\n",
    "    union = np.logical_or(ground_truth_image, np.squeeze(mask))\n",
    "    iou_score = np.sum(intersection) / np.sum(union)\n",
    "\n",
    "    threshold_list.append(threshold)\n",
    "    IoU_scores_list.append(iou_score)\n",
    "\n",
    "  return (threshold_list, IoU_scores_list)\n",
    "\n",
    "\n",
    "\n",
    "# -------------- Other definitions -----------\n",
    "W  = '\\033[0m'  # white (normal)\n",
    "R  = '\\033[31m' # red\n",
    "prediction_prefix = 'Predicted_'\n",
    "\n",
    "\n",
    "print('-------------------')\n",
    "print('U-Net and dependencies installed.')\n",
    "\n",
    "# Colors for the warning messages\n",
    "class bcolors:\n",
    "  WARNING = '\\033[31m'\n",
    "\n",
    "# Check if this is the latest version of the notebook\n",
    "\n",
    "All_notebook_versions = pd.read_csv(\"https://raw.githubusercontent.com/HenriquesLab/ZeroCostDL4Mic/master/Colab_notebooks/Latest_Notebook_versions.csv\", dtype=str)\n",
    "print('Notebook version: '+Notebook_version)\n",
    "Latest_Notebook_version = All_notebook_versions[All_notebook_versions[\"Notebook\"] == Network]['Version'].iloc[0]\n",
    "print('Latest notebook version: '+Latest_Notebook_version)\n",
    "if Notebook_version == Latest_Notebook_version:\n",
    "  print(\"This notebook is up-to-date.\")\n",
    "else:\n",
    "  print(bcolors.WARNING +\"A new version of this notebook has been released. We recommend that you download it at https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki\")\n",
    "\n",
    "\n",
    "def pdf_export(trained = False, augmentation = False, pretrained_model = False):\n",
    "  class MyFPDF(FPDF, HTMLMixin):\n",
    "    pass\n",
    "\n",
    "  pdf = MyFPDF()\n",
    "  pdf.add_page()\n",
    "  pdf.set_right_margin(-1)\n",
    "  pdf.set_font(\"Arial\", size = 11, style='B') \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  day = datetime.now()\n",
    "  datetime_str = str(day)[0:10]\n",
    "\n",
    "  Header = 'Training report for '+Network+' model ('+model_name+')\\nDate: '+datetime_str\n",
    "  pdf.multi_cell(180, 5, txt = Header, align = 'L') \n",
    "    \n",
    "  # add another cell \n",
    "  if trained:\n",
    "    training_time = \"Training time: \"+str(hour)+ \"hour(s) \"+str(mins)+\"min(s) \"+str(round(sec))+\"sec(s)\"\n",
    "    pdf.cell(190, 5, txt = training_time, ln = 1, align='L')\n",
    "  pdf.ln(1)\n",
    "\n",
    "  Header_2 = 'Information for your materials and method:'\n",
    "  pdf.cell(190, 5, txt=Header_2, ln=1, align='L')\n",
    "\n",
    "  all_packages = ''\n",
    "  for requirement in freeze(local_only=True):\n",
    "    all_packages = all_packages+requirement+', '\n",
    "  #print(all_packages)\n",
    "\n",
    "  #Main Packages\n",
    "  main_packages = ''\n",
    "  version_numbers = []\n",
    "  for name in ['tensorflow','numpy','Keras']:\n",
    "    find_name=all_packages.find(name)\n",
    "    main_packages = main_packages+all_packages[find_name:all_packages.find(',',find_name)]+', '\n",
    "    #Version numbers only here:\n",
    "    version_numbers.append(all_packages[find_name+len(name)+2:all_packages.find(',',find_name)])\n",
    "\n",
    "  cuda_version = subprocess.run('nvcc --version',stdout=subprocess.PIPE, shell=True)\n",
    "  cuda_version = cuda_version.stdout.decode('utf-8')\n",
    "  cuda_version = cuda_version[cuda_version.find(', V')+3:-1]\n",
    "  gpu_name = subprocess.run('nvidia-smi',stdout=subprocess.PIPE, shell=True)\n",
    "  gpu_name = gpu_name.stdout.decode('utf-8')\n",
    "  gpu_name = gpu_name[gpu_name.find('Tesla'):gpu_name.find('Tesla')+10]\n",
    "  #print(cuda_version[cuda_version.find(', V')+3:-1])\n",
    "  #print(gpu_name)\n",
    "  loss = str(model.loss)[str(model.loss).find('function')+len('function'):str(model.loss).find('.<')]\n",
    "  shape = io.imread(Training_source+'/'+os.listdir(Training_source)[1]).shape\n",
    "  dataset_size = len(os.listdir(Training_source))\n",
    "\n",
    "  text = 'The '+Network+' model was trained from scratch for '+str(number_of_epochs)+' epochs on '+str(number_of_training_dataset)+' paired image patches (image dimensions: '+str(shape)+', patch size: ('+str(patch_width)+','+str(patch_height)+')) with a batch size of '+str(batch_size)+' and a'+loss+' loss function,'+' using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n",
    "\n",
    "  if pretrained_model:\n",
    "    text = 'The '+Network+' model was trained for '+str(number_of_epochs)+' epochs on '+str(number_of_training_dataset)+' paired image patches (image dimensions: '+str(shape)+', patch size: ('+str(patch_width)+','+str(patch_height)+')) with a batch size of '+str(batch_size)+'  and a'+loss+' loss function,'+' using the '+Network+' ZeroCostDL4Mic notebook (v '+Notebook_version[0]+') (von Chamier & Laine et al., 2020). The model was re-trained from a pretrained model. Key python packages used include tensorflow (v '+version_numbers[0]+'), Keras (v '+version_numbers[2]+'), numpy (v '+version_numbers[1]+'), cuda (v '+cuda_version+'). The training was accelerated using a '+gpu_name+'GPU.'\n",
    "\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "  pdf.multi_cell(180, 5, txt = text, align='L')\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(28, 5, txt='Augmentation: ', ln=1)\n",
    "  pdf.set_font('')\n",
    "  if augmentation:\n",
    "    aug_text = 'The dataset was augmented by'\n",
    "    if rotation_range != 0:\n",
    "      aug_text = aug_text+'\\n- rotation'\n",
    "    if horizontal_flip == True or vertical_flip == True:\n",
    "      aug_text = aug_text+'\\n- flipping'\n",
    "    if zoom_range != 0:\n",
    "      aug_text = aug_text+'\\n- random zoom magnification'\n",
    "    if horizontal_shift != 0 or vertical_shift != 0:\n",
    "      aug_text = aug_text+'\\n- shifting'\n",
    "    if shear_range != 0:\n",
    "      aug_text = aug_text+'\\n- image shearing'\n",
    "  else:\n",
    "    aug_text = 'No augmentation was used for training.'\n",
    "  pdf.multi_cell(190, 5, txt=aug_text, align='L')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(180, 5, txt = 'Parameters', align='L', ln=1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "  if Use_Default_Advanced_Parameters:\n",
    "    pdf.cell(200, 5, txt='Default Advanced Parameters were enabled')\n",
    "  pdf.cell(200, 5, txt='The following parameters were used for training:')\n",
    "  pdf.ln(1)\n",
    "  html = \"\"\" \n",
    "  <table width=40% style=\"margin-left:0px;\">\n",
    "    <tr>\n",
    "      <th width = 50% align=\"left\">Parameter</th>\n",
    "      <th width = 50% align=\"left\">Value</th>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>number_of_epochs</td>\n",
    "      <td width = 50%>{0}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>patch_size</td>\n",
    "      <td width = 50%>{1}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>batch_size</td>\n",
    "      <td width = 50%>{2}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>number_of_steps</td>\n",
    "      <td width = 50%>{3}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>percentage_validation</td>\n",
    "      <td width = 50%>{4}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>initial_learning_rate</td>\n",
    "      <td width = 50%>{5}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>pooling_steps</td>\n",
    "      <td width = 50%>{6}</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td width = 50%>min_fraction</td>\n",
    "      <td width = 50%>{7}</td>\n",
    "  </table>\n",
    "  \"\"\".format(number_of_epochs, str(patch_width)+'x'+str(patch_height), batch_size, number_of_steps, percentage_validation, initial_learning_rate, pooling_steps, min_fraction)\n",
    "  pdf.write_html(html)\n",
    "\n",
    "  #pdf.multi_cell(190, 5, txt = text_2, align='L')\n",
    "  pdf.set_font(\"Arial\", size = 11, style='B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(190, 5, txt = 'Training Dataset', align='L', ln=1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.cell(29, 5, txt= 'Training_source:', align = 'L', ln=0)\n",
    "  pdf.set_font('')\n",
    "  pdf.multi_cell(170, 5, txt = Training_source, align = 'L')\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.cell(28, 5, txt= 'Training_target:', align = 'L', ln=0)\n",
    "  pdf.set_font('')\n",
    "  pdf.multi_cell(170, 5, txt = Training_target, align = 'L')\n",
    "  #pdf.cell(190, 5, txt=aug_text, align='L', ln=1)\n",
    "  pdf.ln(1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.cell(21, 5, txt= 'Model Path:', align = 'L', ln=0)\n",
    "  pdf.set_font('')\n",
    "  pdf.multi_cell(170, 5, txt = model_path+'/'+model_name, align = 'L')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(60, 5, txt = 'Example Training pair', ln=1)\n",
    "  pdf.ln(1)\n",
    "  exp_size = io.imread('/content/TrainingDataExample_Unet2D.png').shape\n",
    "  pdf.image('/content/TrainingDataExample_Unet2D.png', x = 11, y = None, w = round(exp_size[1]/8), h = round(exp_size[0]/8))\n",
    "  pdf.ln(1)\n",
    "  ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"Democratising deep learning for microscopy with ZeroCostDL4Mic.\" Nature Communications (2021).'\n",
    "  pdf.multi_cell(190, 5, txt = ref_1, align='L')\n",
    "  ref_2 = '- Unet: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.'\n",
    "  pdf.multi_cell(190, 5, txt = ref_2, align='L')\n",
    "  # if Use_Data_augmentation:\n",
    "  #   ref_3 = '- Augmentor: Bloice, Marcus D., Christof Stocker, and Andreas Holzinger. \"Augmentor: an image augmentation library for machine learning.\" arXiv preprint arXiv:1708.04680 (2017).'\n",
    "  #   pdf.multi_cell(190, 5, txt = ref_3, align='L')\n",
    "  pdf.ln(3)\n",
    "  reminder = 'Important:\\nRemember to perform the quality control step on all newly trained models\\nPlease consider depositing your training dataset on Zenodo'\n",
    "  pdf.set_font('Arial', size = 11, style='B')\n",
    "  pdf.multi_cell(190, 5, txt=reminder, align='C')\n",
    "\n",
    "  pdf.output(model_path+'/'+model_name+'/'+model_name+'_training_report.pdf')\n",
    "\n",
    "  print('------------------------------')\n",
    "  print('PDF report exported in '+model_path+'/'+model_name+'/')\n",
    "\n",
    "def qc_pdf_export():\n",
    "  class MyFPDF(FPDF, HTMLMixin):\n",
    "    pass\n",
    "\n",
    "  pdf = MyFPDF()\n",
    "  pdf.add_page()\n",
    "  pdf.set_right_margin(-1)\n",
    "  pdf.set_font(\"Arial\", size = 11, style='B') \n",
    "\n",
    "  Network = 'Unet 2D'\n",
    "\n",
    "  day = datetime.now()\n",
    "  datetime_str = str(day)[0:10]\n",
    "\n",
    "  Header = 'Quality Control report for '+Network+' model ('+QC_model_name+')\\nDate: '+datetime_str\n",
    "  pdf.multi_cell(180, 5, txt = Header, align = 'L') \n",
    "\n",
    "  all_packages = ''\n",
    "  for requirement in freeze(local_only=True):\n",
    "    all_packages = all_packages+requirement+', '\n",
    "\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(2)\n",
    "  pdf.cell(190, 5, txt = 'Loss curves', ln=1, align='L')\n",
    "  pdf.ln(1)\n",
    "  exp_size = io.imread(full_QC_model_path+'/Quality Control/QC_example_data.png').shape\n",
    "  if os.path.exists(full_QC_model_path+'/Quality Control/lossCurvePlots.png'):\n",
    "    pdf.image(full_QC_model_path+'/Quality Control/lossCurvePlots.png', x = 11, y = None, w = round(exp_size[1]/12), h = round(exp_size[0]/3))\n",
    "  else:\n",
    "    pdf.set_font('')\n",
    "    pdf.set_font('Arial', size=10)\n",
    "    pdf.multi_cell(190, 5, txt='If you would like to see the evolution of the loss function during training please play the first cell of the QC section in the notebook.',align='L')\n",
    "  pdf.ln(2)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(2)\n",
    "  pdf.cell(190, 5, txt = 'Threshold Optimisation', ln=1, align='L')\n",
    "  #pdf.ln(1)\n",
    "  exp_size = io.imread(full_QC_model_path+'/Quality Control/'+QC_model_name+'_IoUvsThresholdPlot.png').shape\n",
    "  pdf.image(full_QC_model_path+'/Quality Control/'+QC_model_name+'_IoUvsThresholdPlot.png', x = 11, y = None, w = round(exp_size[1]/6), h = round(exp_size[0]/7))\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 10, style = 'B')\n",
    "  pdf.ln(3)\n",
    "  pdf.cell(80, 5, txt = 'Example Quality Control Visualisation', ln=1)\n",
    "  pdf.ln(1)\n",
    "  exp_size = io.imread(full_QC_model_path+'/Quality Control/QC_example_data.png').shape\n",
    "  pdf.image(full_QC_model_path+'/Quality Control/QC_example_data.png', x = 16, y = None, w = round(exp_size[1]/8), h = round(exp_size[0]/8))\n",
    "  pdf.ln(1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font('Arial', size = 11, style = 'B')\n",
    "  pdf.ln(1)\n",
    "  pdf.cell(180, 5, txt = 'Quality Control Metrics', align='L', ln=1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "\n",
    "  pdf.ln(1)\n",
    "  html = \"\"\"\n",
    "  <body>\n",
    "  <font size=\"10\" face=\"Courier New\" >\n",
    "  <table width=60% style=\"margin-left:0px;\">\"\"\"\n",
    "  with open(full_QC_model_path+'/Quality Control/QC_metrics_'+QC_model_name+'.csv', 'r') as csvfile:\n",
    "    metrics = csv.reader(csvfile)\n",
    "    header = next(metrics)\n",
    "    image = header[0]\n",
    "    IoU = header[1]\n",
    "    IoU_OptThresh = header[2]\n",
    "    header = \"\"\"\n",
    "    <tr>\n",
    "    <th width = 33% align=\"center\">{0}</th>\n",
    "    <th width = 33% align=\"center\">{1}</th>\n",
    "    <th width = 33% align=\"center\">{2}</th>\n",
    "    </tr>\"\"\".format(image,IoU,IoU_OptThresh)\n",
    "    html = html+header\n",
    "    i=0\n",
    "    for row in metrics:\n",
    "      i+=1\n",
    "      image = row[0]\n",
    "      IoU = row[1]\n",
    "      IoU_OptThresh = row[2]\n",
    "      cells = \"\"\"\n",
    "        <tr>\n",
    "          <td width = 33% align=\"center\">{0}</td>\n",
    "          <td width = 33% align=\"center\">{1}</td>\n",
    "          <td width = 33% align=\"center\">{2}</td>\n",
    "        </tr>\"\"\".format(image,str(round(float(IoU),3)),str(round(float(IoU_OptThresh),3)))\n",
    "      html = html+cells\n",
    "    html = html+\"\"\"</body></table>\"\"\"\n",
    "    \n",
    "  pdf.write_html(html)\n",
    "\n",
    "  pdf.ln(1)\n",
    "  pdf.set_font('')\n",
    "  pdf.set_font_size(10.)\n",
    "  ref_1 = 'References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, Romain, et al. \"Democratising deep learning for microscopy with ZeroCostDL4Mic.\" Nature Communications (2021).'\n",
    "  pdf.multi_cell(190, 5, txt = ref_1, align='L')\n",
    "  ref_2 = '- Unet: Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. \"U-net: Convolutional networks for biomedical image segmentation.\" International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.'\n",
    "  pdf.multi_cell(190, 5, txt = ref_2, align='L')\n",
    "\n",
    "  pdf.ln(3)\n",
    "  reminder = 'To find the parameters and other information about how this model was trained, go to the training_report.pdf of this model which should be in the folder of the same name.'\n",
    "\n",
    "  pdf.set_font('Arial', size = 11, style='B')\n",
    "  pdf.multi_cell(190, 5, txt=reminder, align='C')\n",
    "\n",
    "  pdf.output(full_QC_model_path+'/Quality Control/'+QC_model_name+'_QC_report.pdf')\n",
    "\n",
    "  print('------------------------------')\n",
    "  print('QC PDF report exported as '+full_QC_model_path+'/Quality Control/'+QC_model_name+'_QC_report.pdf')\n",
    "\n",
    "# Build requirements file for local run\n",
    "after = [str(m) for m in sys.modules]\n",
    "build_requirements_file(before, after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2646cce",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0000.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0001.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0002.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0003.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0004.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0005.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0006.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0007.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0008.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0009.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0010.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0011.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0012.tif', 'TropismoRenal_2020_raton1D_25jun21_25Jun2021_Acq001_ring0013.tif', 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      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b86f703c85d481cbf873c8e0a05ebe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0000.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0001.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0002.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0003.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0004.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0005.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0006.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0007.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0008.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0009.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0010.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0011.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0012.tif', 'TropismoRenal_2020_raton1I_25jun21_25Jun2021_Acq001_ring0013.tif', 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      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa13800c46e445f0a0b4ac9bdc5582eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0000.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0001.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0002.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0003.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0004.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0005.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0006.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0007.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0008.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0009.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0010.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0011.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0012.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0013.tif', 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'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0462.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0463.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0464.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0465.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0466.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0467.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0468.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0469.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0470.tif', 'TROPISMORENAL_2020_RATON2D_25JUN21_25Jun2021_Acq001_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0682bdd6c3048e39fa96c6db4d12a65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
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      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "494955b1c84c48ffa4958b239d0117be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0000.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0001.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0002.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0003.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0004.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0005.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0006.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0007.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0008.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0009.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0010.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0011.tif', 'TROPISMORENAL_2020_RATON_CONTROL_25JUN21_25Jun2021_Acq001_ring0012.tif', 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      "Number of dataset found in the folder: 476\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39bbd88b6a6e44fcbd2d88ca08d492e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/476 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0000.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0001.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0002.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0003.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0004.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0005.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0006.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0007.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0008.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0009.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0010.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0011.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0012.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0013.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0014.tif', 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'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0465.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0466.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0467.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0468.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0469.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0470.tif', 'TROPISMORENAL_RATON_1D_13JUL21_13Jul2021_Acq001_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "26a385be062d41b2a6ca2664d94b335c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0000.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0001.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0002.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0003.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0004.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0005.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0006.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0007.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0008.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0009.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0010.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0011.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0012.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0013.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0014.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0015.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0016.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0017.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0018.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0019.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0020.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0021.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0022.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0023.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0024.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0025.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0026.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0027.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0028.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0029.tif', 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'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0450.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0451.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0452.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0453.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0454.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0455.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0456.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0457.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0458.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0459.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0460.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0461.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0462.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0463.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0464.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0465.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0466.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0467.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0468.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0469.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0470.tif', 'TROPISMORENAL_RATON_1I_13JUL21_13Jul2021_Acq001_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eaf20f75b9e64b21ae8763fe21d2031f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0000.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0001.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0002.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0003.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0004.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0005.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0006.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0007.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0008.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0009.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0010.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0011.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0012.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0013.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0014.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0015.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0016.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0017.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0018.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0019.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0020.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0021.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0022.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0023.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0024.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0025.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0026.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0027.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0028.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0029.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0030.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0031.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0032.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0033.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0034.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0035.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0036.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0037.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0038.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0039.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0040.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0041.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0042.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0043.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0044.tif', 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'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0465.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0466.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0467.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0468.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0469.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0470.tif', 'TROPISMORENAL_RATON_2D_13JUL21_13Jul2021_Acq001_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a69723213ada4ce6a3884d7d96ade5dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0000.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0001.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0002.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0003.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0004.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0005.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0006.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0007.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0008.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0009.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0010.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0011.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0012.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0013.tif', 'TropismoRenal_raton_2I_13jul21_13Jul2021_Acq001_ring0014.tif', 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      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f79998d7f0b547ebb98ed69dc08fb0e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
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'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0448.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0449.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0450.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0451.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0452.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0453.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0454.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0455.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0456.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0457.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0458.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0459.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0460.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0461.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0462.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0463.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0464.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0465.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0466.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0467.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0468.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0469.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0470.tif', 'TROPISMORENAL_RATON_CONTROL_13JUL21_13Jul2021_Acq001_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "644c5f4bd127427c96e83fec4bd703c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0000.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0001.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0002.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0003.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0004.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0005.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0006.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0007.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0008.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0009.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0010.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0011.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0012.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0013.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0014.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0015.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0016.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0017.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0018.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0019.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0020.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0021.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0022.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0023.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0024.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0025.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0026.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0027.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0028.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0029.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0030.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0031.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0032.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0033.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0034.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0035.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0036.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0037.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0038.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0039.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0040.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0041.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0042.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0043.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0044.tif', 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'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0465.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0466.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0467.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0468.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0469.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0470.tif', 'TROPISMORENAL_RATON_1D_28JUL21_28Jul2021_Acq001_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fcdfcd6317140639168d6c736d078c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n",
      "The cysts4-Imagenet-pretrained(fromEpoch134) network will be used.\n",
      "Model input size: 512x512\n",
      "['TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0000.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0001.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0002.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0003.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0004.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0005.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0006.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0007.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0008.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0009.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0010.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0011.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0012.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0013.tif', 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'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0462.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0463.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0464.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0465.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0466.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0467.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0468.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0469.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0470.tif', 'TROPISMORENAL_RATON_CONTROL_28JUL21_28Jul2021_Acq002_ring0471.tif']\n",
      "Number of dataset found in the folder: 472\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d050c4a19ad24ba9a1d944164b3cf999",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/472 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Apply the unet to the data, otherwise don't run.\n",
    "\n",
    "for count,Data_folder in enumerate(Data_folders):\n",
    "    \n",
    "    #Fill the name of the folder to correctly get the images\n",
    "    Data_folder = Data_folder + '/' + os.path.basename(Data_folder)\n",
    "    Results_folder = Results_folders+'/'+ os.path.basename(Data_folder)+'-Results'\n",
    "    \n",
    "    os.makedirs(Results_folder)\n",
    "    #Run the Unet on the images\n",
    "\n",
    "    # ------------- Initial user input ------------\n",
    "    #@markdown ###Provide the path to your dataset and to the folder where the prediction will be saved (Result folder), then play the cell to predict output on your unseen images.\n",
    "\n",
    "\n",
    "    #@markdown ###Do you want to use the current trained model?\n",
    "    Use_the_current_trained_model = False #@param {type:\"boolean\"}\n",
    "\n",
    "    #@markdown ###If not, please provide the path to the model folder:\n",
    "\n",
    "\n",
    "    #Here we find the loaded model name and parent path\n",
    "    Prediction_model_name = os.path.basename(Prediction_model_folder)\n",
    "    Prediction_model_path = os.path.dirname(Prediction_model_folder)\n",
    "\n",
    "\n",
    "    # ------------- Failsafes ------------\n",
    "    if (Use_the_current_trained_model): \n",
    "      print(\"Using current trained network\")\n",
    "      Prediction_model_name = model_name\n",
    "      Prediction_model_path = model_path\n",
    "\n",
    "    full_Prediction_model_path = os.path.join(Prediction_model_path, Prediction_model_name)\n",
    "    if os.path.exists(full_Prediction_model_path):\n",
    "      print(\"The \"+Prediction_model_name+\" network will be used.\")\n",
    "    else:\n",
    "      print(R+'!! WARNING: The chosen model does not exist !!'+W)\n",
    "      print('Please make sure you provide a valid model path and model name before proceeding further.')\n",
    "\n",
    "\n",
    "    # ------------- Prepare the model and run predictions ------------\n",
    "\n",
    "    # Load the model and prepare generator\n",
    "\n",
    "\n",
    "\n",
    "    unet = load_model(os.path.join(Prediction_model_path, Prediction_model_name, 'weights_best.hdf5'), custom_objects={'_weighted_binary_crossentropy': weighted_binary_crossentropy(np.ones(2))})\n",
    "    Input_size = unet.layers[0].output_shape[0][1:3]\n",
    "    print('Model input size: '+str(Input_size[0])+'x'+str(Input_size[1]))\n",
    "\n",
    "    # Create a list of sources\n",
    "    source_dir_list = os.listdir(Data_folder)\n",
    "    print(source_dir_list)\n",
    "    number_of_dataset = len(source_dir_list)\n",
    "    print('Number of dataset found in the folder: '+str(number_of_dataset))\n",
    "\n",
    "    predictions = []\n",
    "    for i in tqdm(range(number_of_dataset)):\n",
    "      predictions.append(predict_as_tiles(os.path.join(Data_folder, source_dir_list[i]), unet))\n",
    "      # predictions.append(prediction(os.path.join(Data_folder, source_dir_list[i]), os.path.join(Prediction_model_path, Prediction_model_name)))\n",
    "\n",
    "\n",
    "    # Save the results in the folder along with the masks according to the set threshold\n",
    "    saveResult(Results_folder, predictions, source_dir_list, prefix=prediction_prefix, threshold=None)\n",
    "\n",
    "\n",
    "    # ------------- For display ------------\n",
    "    print('--------------------------------------------------------------')\n",
    "\n",
    "\n",
    "    def show_prediction_mask(file=os.listdir(Data_folder), threshold=(0,255,1)):\n",
    "\n",
    "      plt.figure(figsize=(18,6))\n",
    "      # Wide-field\n",
    "      plt.subplot(1,3,1)\n",
    "      plt.axis('off')\n",
    "      img_Source = plt.imread(os.path.join(Data_folder, file))\n",
    "      plt.imshow(img_Source, cmap='gray')\n",
    "      plt.title('Source image',fontsize=15)\n",
    "      # Prediction\n",
    "      plt.subplot(1,3,2)\n",
    "      plt.axis('off')\n",
    "      img_Prediction = plt.imread(os.path.join(Results_folder, prediction_prefix+file))\n",
    "      plt.imshow(img_Prediction, cmap='gray')\n",
    "      plt.title('Prediction',fontsize=15)\n",
    "\n",
    "      # Thresholded mask\n",
    "      plt.subplot(1,3,3)\n",
    "      plt.axis('off')\n",
    "      img_Mask = convert2Mask(img_Prediction, threshold)\n",
    "      plt.imshow(img_Mask, cmap='gray')\n",
    "      plt.title('Mask (Threshold: '+str(round(threshold))+')',fontsize=15)\n",
    "\n",
    "\n",
    "    #interact(show_prediction_mask, continuous_update=True);\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43715700",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diseased region analysis\n",
      "imOrig3D.shape after histogram equalization:  (475, 514, 514)\n",
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
      "os.path.basename(Data_folder):  TROPISMORENAL_RATON2_1D_26NOV21_26Nov2021_Acq001_ring\n",
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
      "THRESHOLD:  55.0\n",
      "Otsu threshold applied\n",
      "Full kidney mask eroded\n",
      "np.min(distance):  0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\equipo\\.conda\\envs\\workenvpy3.7\\lib\\site-packages\\ipykernel_launcher.py:266: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 3D .tif stacks have been successfully saved\n",
      "Diseased region analysis\n",
      "imOrig3D.shape after histogram equalization:  (475, 514, 514)\n",
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
      "os.path.basename(Data_folder):  TROPISMORENAL_RATON3_1I_26NOV21_26Nov2021_Acq001_ring\n",
      ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n",
      "THRESHOLD:  57.0\n",
      "Otsu threshold applied\n",
      "Full kidney mask eroded\n",
      "np.min(distance):  0.0\n",
      "The 3D .tif stacks have been successfully saved\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Run this cell to work with manually corrected masks, \n",
    "\n",
    "#Alternative Image postprocessing after deep learning segmentation. \n",
    "#Different segmentation for the cysts (added options \n",
    "#to work with manually corrected masks)\n",
    "\n",
    "thresImages = list()\n",
    "thresholds = np.zeros(len(Data_folders))\n",
    "numIters = 1000\n",
    "\n",
    "for count,Data_folder in enumerate(Data_folders):\n",
    "    #if  'TropismoRenal2020_raton6_5enero22_05Jan2022_Acq004_ring' in Data_folder:\n",
    "    #if count<=numIters\n",
    "    Data_folder = Data_folder + '/' + os.path.basename(Data_folder)\n",
    "    #Fill the name of the folder to correctly get the images\n",
    "    \n",
    "    #If the input masks are the ones directly provided by the unet\n",
    "    if not manually_corr_kidneysMask is True:\n",
    "        Results_folder = Results_folders+'/'+ os.path.basename(Data_folder)+'-Results'\n",
    "\n",
    "        #-----------------------------------------------------------------------------------------------\n",
    "        #-----------------------------------------------------------------------------------------------\n",
    "        #Postprocessing and volume calculations\n",
    "\n",
    "\n",
    "        #This script takes as an input the folder with masks coming from applying Unet2 to kidney images.\n",
    "        #It thresholds them to convert them to binary, puts them together in a single stack and \n",
    "        #postprocesses them to get rid of connected components smaller than the kidneys. Then it calculates\n",
    "        #the percentage of diseased kidney.\n",
    "\n",
    "        print('Full kidney mask refinement')\n",
    "        dataPath = Results_folder\n",
    "\n",
    "        print('dataPath: ',dataPath)\n",
    "\n",
    "        imSlices = glob(dataPath+'/*.tif')\n",
    "\n",
    "        #print('imSlices: ',imSlices)\n",
    "\n",
    "        #Get dimensions from one of the images and create an empty 3D array\n",
    "        sl = tiff.imread(imSlices[0])\n",
    "        im3D = np.zeros([len(imSlices),sl.shape[0],sl.shape[1]],dtype=np.uint16)\n",
    "\n",
    "        #Scan the folder through the images and save them in the array (im3D)\n",
    "        for zPos,slName in enumerate(imSlices):\n",
    "\n",
    "            sl = tiff.imread(slName)\n",
    "            im3D[zPos,:,:] = sl\n",
    "\n",
    "        print('im3D.shape: ',im3D.shape)\n",
    "\n",
    "\n",
    "\n",
    "        #If the image is not already binary\n",
    "        if len(np.unique(im3D))>2:\n",
    "            print('Binarized')\n",
    "            #Threshold the 3D image above half the maximum value to get a binary mask, converts it to uint8 for\n",
    "            #efficiency\n",
    "            print('Threshold used: ',threshold)\n",
    "            im3D = np.uint8(1*(im3D>=threshold))\n",
    "\n",
    "        #If it is binary, just take the values greater than 0\n",
    "        else:\n",
    "            im3D = np.uint16(1*(im3D>0))\n",
    "\n",
    "\n",
    "        #Apply connected components to keep only the kidneys    \n",
    "        im3DLabeled = cc3d.connected_components(im3D, connectivity=6)\n",
    "\n",
    "        stats = cc3d.statistics(im3DLabeled)    \n",
    "\n",
    "        voxelCounts = stats['voxel_counts']\n",
    "        #Obtained the sorted indices for all connected components, sorted by size (ascending order)\n",
    "        sortIndices = np.argsort(voxelCounts)\n",
    "\n",
    "        #Remove all components but the second and third largest (the kidneys, since the largest is\n",
    "        #the background)\n",
    "        im3DLabeled[(im3DLabeled!=sortIndices[-2]) & (im3DLabeled!=sortIndices[-3])]=0\n",
    "\n",
    "        #Create the binary version\n",
    "        im3D = im3DLabeled\n",
    "        im3D[im3D>0]=1\n",
    "        im3D=np.uint16(im3D)\n",
    "        \n",
    "        #Morphological closing not necessary with manually corrected masks\n",
    "        \n",
    "        #Apply morphological closing of the specified \"radius\" to remove holes, slice by slice\n",
    "        #kernelClosing = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(radiusClosing*2+1,radiusClosing*2+1))    \n",
    "\n",
    "        #for i in range(im3D.shape[0]):\n",
    "        #    im3D[i,:,:] =  cv2.morphologyEx(im3D[i,:,:], cv2.MORPH_CLOSE, kernelClosing)    \n",
    "\n",
    "    #On the other hand, if you want to obtain the cysts from already manually corrected masks.           \n",
    "    elif manually_corr_kidneysMask is True: \n",
    "        #Here is the manually corrected kidney's mask\n",
    "        dataPath = glob(os.path.dirname(Data_folder)+'/*Corrected*fullKidneysMask*.tif')[0]\n",
    "        im3D=tiff.imread(dataPath)\n",
    "    \n",
    "    #-----------------------------------------------------------------------------\n",
    "    print('Diseased region analysis')\n",
    "    #Analyze the diseased region\n",
    "\n",
    "\n",
    "\n",
    "    #Create a 3D array with the original image\n",
    "    imSlices = glob(Data_folder+'/*.tif')\n",
    "    sl = tiff.imread(imSlices[0])\n",
    "    imOrig3D = np.zeros([len(imSlices),sl.shape[0],sl.shape[1]])\n",
    "\n",
    "    #Scan the folder through the original images and save them in the array (imOrig3D)\n",
    "    for zPos,slName in enumerate(imSlices):\n",
    "\n",
    "        sl = tiff.imread(slName)\n",
    "        imOrig3D[zPos,:,:] = sl\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.imshow(imOrig3D[slice1,:,:])\n",
    "    plt.title('imOrig3D before any processing'+'-'+str(imOrig3D.shape))\n",
    "        \n",
    "    #imOrig3D = np.uint16(imOrig3D)   \n",
    "    \n",
    "\n",
    "    \n",
    "    #plt.figure(figsize=(10,10))\n",
    "    #plt.subplot(1,2,1)\n",
    "    #plt.title('Original')\n",
    "    #plt.imshow(imOrig3D[slice1,180:320,140:380])\n",
    "    #Keep only the values inside the mask\n",
    "    #tiff.imsave(dataPath+'-origNoGaussianFiltered.tif', imOrig3D)\n",
    "\n",
    "    #Filter the image with a Gaussian kernel\n",
    "    imOrig3D = scipy.ndimage.gaussian_filter(imOrig3D,sigma=1)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.imshow(imOrig3D[slice1,:,:])\n",
    "    plt.title('gaussian filtered'+'-'+str(imOrig3D.shape))\n",
    "    #tiff.imsave(dataPath+'-origGaussianFiltered.tif', imOrig3D)\n",
    "    \n",
    "    \n",
    "    maskRegion = imOrig3D[im3D>0]\n",
    "    #plt.subplot(1,2,2)\n",
    "    #plt.title('Filtered')\n",
    "    #plt.imshow(imOrig3D[slice1,180:320,140:380])  \n",
    "    #Perform histogram equalization with the segmentation's histogram\n",
    "    #Keep only the values inside the mask\n",
    "    maskRegion = imOrig3D[im3D>0]\n",
    "\n",
    "    #hist,bins = np.histogram(maskRegion,bins = 256, density=True)\n",
    "    hist,bins = np.histogram(maskRegion,bins = 256, density=True)\n",
    "    bins = np.float16(bins)\n",
    "    cdf = hist.cumsum()\n",
    "    #plt.figure(figsize=(20,20))\n",
    "    #plt.subplot(1,2,1)\n",
    "    #plt.plot(cdf/cdf[-1], color = 'g')\n",
    "    #plt.title('Cumulative distribution function of the original image (inside the kidney)'\\\n",
    "    #          ,color='white',fontsize=20)\n",
    "    #plt.hist(cutIm.flatten(),256, color = 'r')\n",
    "\n",
    "    imOrigNoEqualiz = imOrig3D.copy()\n",
    "    shp = imOrig3D.shape\n",
    "\n",
    "    cdf = 255 * cdf / cdf[-1]\n",
    "    cdf = np.uint8(cdf)\n",
    "    imOrig3D = np.interp(imOrig3D.flatten(), bins[:-1], cdf)\n",
    "    imOrig3D = imOrig3D.reshape(shp)\n",
    "    imOrig3D = np.uint16(imOrig3D)\n",
    "    hist,bins = np.histogram(imOrig3D[im3D>0],bins = 500)\n",
    "    bins = np.float16(bins)\n",
    "    cdf = hist.cumsum()\n",
    "\n",
    "    print('imOrig3D.shape after histogram equalization: ',imOrig3D.shape)\n",
    "    \n",
    "    #plt.subplot(1,2,2)\n",
    "    #plt.plot(cdf/cdf[-1], color = 'g')\n",
    "    #plt.title('Cumulative distribution function of the equalized image (inside the kidney)'\\\n",
    "    #          ,color='white',fontsize=20)\n",
    "    #plt.hist(cutIm.flatten(),256, color = 'r')        \n",
    "\n",
    "    maskRegion = imOrig3D[im3D>0]\n",
    "\n",
    "    #plt.figure()\n",
    "    #plt.title('Equalized histogram image')\n",
    "    #plt.imshow(imOrig3D[slice1,180:320,140:380])\n",
    "    #equ=equ.reshape(imOrig3D.shape)\n",
    "\n",
    "\n",
    "\n",
    "    #Divide the kidneys in 2 regions using the Otsu method. The darkest region takes some surrounding\n",
    "    #background (the mask is overextended) + the diseased area\n",
    "    #Calculating these thresholds before eroding the mask gives a better result\n",
    "    \n",
    "    #Correcting a specific mask that gave poor results\n",
    "    \n",
    "    print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')\n",
    "    print('os.path.basename(Data_folder): ',os.path.basename(Data_folder))\n",
    "    print('>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>')\n",
    "\n",
    "\n",
    "    ret,th = cv2.threshold(maskRegion,0,np.max(maskRegion),cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "    ret2,th = cv2.threshold(maskRegion[maskRegion<ret],0,np.max(maskRegion[maskRegion<ret]), \\\n",
    "                            cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "\n",
    "    #Save the threshold in a general thresholds vector and a list with the corresponding images names\n",
    "    thresholds[count] = ret2\n",
    "    \n",
    "    print('THRESHOLD: ',ret2)\n",
    "    thresImages.append(os.path.basename(Data_folder))\n",
    "\n",
    "    #Create a new mask marking only the surrounding background + diseased tissue (the darkest area)\n",
    "    newMask = np.zeros(imOrig3D.shape)\n",
    "    newMask [imOrig3D<ret2] = 1\n",
    "    print('Otsu threshold applied')\n",
    "    plt.figure()\n",
    "    plt.imshow(newMask[slice1,:,:])\n",
    "    plt.title('newMask: '+os.path.basename(Data_folder)+'-'+str(newMask.shape))\n",
    "    \n",
    "\n",
    "    kernelErosion = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(radiusErosion*2+1,radiusErosion*2+1))  \n",
    "    kernelSmallOpening = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(1,1))  \n",
    "    for i in range(im3D.shape[0]):\n",
    "        #Erode the original kidney mask to reduce the overextension outside the kidney.\n",
    "        im3DEroded = cv2.morphologyEx(im3D[i,:,:], cv2.MORPH_ERODE, kernelErosion) \n",
    "        #Keep the new mask only inside the eroded full kidney mask\n",
    "        newMask[i,:,:] = newMask[i,:,:]*im3DEroded\n",
    "    print('Full kidney mask eroded')\n",
    "\n",
    "\n",
    "    #newMask = np.uint8(newMask)\n",
    "\n",
    "    #The low intensity pixels threshold is obtained in all the original kidney segmentation. Then,\n",
    "    #reduced to the area of the eroded segmentation.\n",
    "    #The segmentation that is saved for the full kidneys is the complete one, not eroded.\n",
    "\n",
    "\n",
    "    diseasedArea = np.sum(newMask==1)\n",
    "\n",
    "    #Use watershed to make sure both kidneys are separated\n",
    "\n",
    "\n",
    "\n",
    "    distance = ndimage.distance_transform_edt(im3D)\n",
    "    print('np.min(distance): ',np.min(distance))\n",
    "\n",
    "    coords = feature.peak_local_max(distance, labels=im3D,min_distance=50)\n",
    "    mask = np.zeros(distance.shape)\n",
    "    #Fill the values of the maxima points inside mask\n",
    "    mask[tuple(coords.T)] = distance[tuple(coords.T)]\n",
    "\n",
    "    #Get the second largest maximum and make 0 the smaller ones (to keep only 2 maxima, the kidneys)\n",
    "\n",
    "    mask[mask<np.partition(mask.flatten(), -2)[-2]]=0\n",
    "\n",
    "    #Make true the 2 remaining maxima in mask\n",
    "    mask[mask>0] = True\n",
    "\n",
    "\n",
    "\n",
    "    markers, _ = ndimage.label(mask)\n",
    "    #Perform watershed to separate the kidneys if necessary\n",
    "    im3DLabeledPrev = segmentation.watershed(-distance, markers, mask=im3D)\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.imshow(im3DLabeledPrev[slice1,:,:])\n",
    "    plt.title('im3DLabeledPrev: '+os.path.basename(Data_folder))\n",
    "    \n",
    "    midXPoint = np.int(np.round(im3DLabeledPrev.shape[1]/2))\n",
    "\n",
    "    im3DLabeled = np.zeros(im3D.shape,dtype='int')\n",
    "\n",
    "    #Relabel the full kidney volumes. 1 for the left kidney, 2 for the right kidney\n",
    "    #If the second largest connected component is the kidney in the left of the image (left kidney)\n",
    "\n",
    "    #If there are more pixels labeled as 1 in the left than in the right side\n",
    "    if np.sum(1*(im3DLabeledPrev[:,:,:midXPoint]==1))>np.sum(1*(im3DLabeledPrev[:,midXPoint:,:]==1)):\n",
    "        #Keep the left kidney as 1\n",
    "        im3DLabeled[im3DLabeledPrev==1]=1\n",
    "        #And the right kidney as 2\n",
    "        im3DLabeled[im3DLabeledPrev==2]=2\n",
    "\n",
    "        #Otherwise invert the labels\n",
    "    else:\n",
    "        #The other way around\n",
    "        im3DLabeled[im3DLabeledPrev==1]=2\n",
    "        #And the left kidney as 2\n",
    "        im3DLabeled[im3DLabeledPrev==2]=1\n",
    "\n",
    "    #Relabel the diseased regions according to the kidney they belong to (1 or 2)\n",
    "    newMaskLabeled = newMask*im3DLabeled\n",
    "\n",
    "    #Showing the original image with the kidney and disease region masks overlapped\n",
    "    plt.figure(figsize=(20,20))\n",
    "    plt.subplot(1,3,1)\n",
    "    #rescaledOrigIm = imOrigNoEqualiz/np.max(imOrigNoEqualiz)*255\n",
    "    rescaledOrigIm = imOrig3D/np.max(imOrig3D)*255\n",
    "    rescaledOrig2D = rescaledOrigIm[slice1,180:320,140:380]\n",
    "    #comp=np.stack(((255*im3D[slice1,180:320,140:380]+rescaledOrig2D)/2,rescaledOrig2D/2,\\\n",
    "    #              rescaledOrig2D/2),axis=2)\n",
    "    comp = im3D[slice1,180:320,140:380]\n",
    "    \n",
    "    comp.astype('uint8')\n",
    "    plt.imshow(comp)\n",
    "\n",
    "    plt.subplot(1,3,2)\n",
    "    #comp=np.stack((255*newMaskLabeled[slice1,180:320,140:380]+rescaledOrig2D,rescaledOrig2D,\\\n",
    "    #               rescaledOrig2D), axis=2)\n",
    "    \n",
    "    comp = newMaskLabeled[slice1,180:320,140:380]\n",
    "    comp.astype('uint8')\n",
    "    plt.imshow(comp)\n",
    "\n",
    "    plt.subplot(1,3,3)\n",
    "    \n",
    "    plt.imshow(rescaledOrig2D)\n",
    "    \n",
    "    #Calculate kidney volumes and diseased volumes\n",
    "    kidneyVolumes = np.array([np.sum(im3DLabeled == 1),np.sum(im3DLabeled == 2)])\n",
    "    totalKidneyVolume = np.sum(kidneyVolumes)\n",
    "\n",
    "    diseasedVolumes = np.array([np.sum(newMaskLabeled == 1),np.sum(newMaskLabeled == 2)])\n",
    "    totalDiseasedVolume = np.sum(diseasedVolumes)\n",
    "\n",
    "    diseasedProp = np.float16(diseasedVolumes/kidneyVolumes)\n",
    "    totalDiseasedProp = np.float16(totalDiseasedVolume/totalKidneyVolume)\n",
    "\n",
    "    \n",
    "    if not manually_corr_kidneysMask is True:\n",
    "    \n",
    "        with open(Results_folder+'-measures(in_voxels).txt', 'w') as f:\n",
    "            f.write('kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,'+\\\n",
    "                    'diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,'+\\\n",
    "                    'mmVoxSize0,mmVoxSize1,mmVoxSize2\\n')\n",
    "            f.write(str(kidneyVolumes[0])+','+str(kidneyVolumes[1])+','+str(totalKidneyVolume)+','+\\\n",
    "                    str(diseasedVolumes[0])+','+str(diseasedVolumes[1])+','+str(totalDiseasedVolume)+','+\\\n",
    "                    str(diseasedProp[0])+','+str(diseasedProp[1])+','+str(totalDiseasedProp)+','+\\\n",
    "                    str(ret2)+','+str(voxelSize[0])+','+str(voxelSize[1])+','+str(voxelSize[2]))\n",
    "\n",
    "\n",
    "\n",
    "        #Save the kidney volumes and diseased regions\n",
    "        tiff.imsave(dataPath+'.tif', np.uint8(im3DLabeled))\n",
    "\n",
    "        tiff.imsave(dataPath+'-diseased.tif',np.uint8(newMaskLabeled))\n",
    "        \n",
    "    elif manually_corr_kidneysMask is True:\n",
    "        tiff.imsave(os.path.dirname(dataPath)+'/inferenceCorrected-'+os.path.basename(Data_folder)\\\n",
    "                    +'-cystsMask.tif',np.uint8(newMaskLabeled))\n",
    "\n",
    "    print('The 3D .tif stacks have been successfully saved')\n",
    "\n",
    "    #Remove the folder with the Unet results\n",
    "    #shutil.rmtree(Results_folder)\n",
    "\n",
    "#Create a folder with the thresholds\n",
    "if not manually_corr_kidneysMask is True:    \n",
    "    with open(Results_folders+'/thresholds.txt', 'a+') as f:\n",
    "        f.seek(0)\n",
    "        text = f.read(100)\n",
    "        #If the file is not empty, add a new line\n",
    "        if len(text) > 0 : \n",
    "            f.write('\\n')\n",
    "        #Write the new line\n",
    "        f.write(str(thresholds))\n",
    "\n",
    "    with open(Results_folders+'/thresImages.txt', 'a+') as f:\n",
    "        f.seek(0)\n",
    "        text = f.read(100)\n",
    "        #If the file is not empty, add a new line\n",
    "        if len(text) > 0 : \n",
    "            f.write('\\n')    \n",
    "        #Write the new line\n",
    "        f.write(str(thresImages))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "862a2199",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TROPISMORENAL2020_RATON1_5ENERO22_05Jan2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "140680,139682,280362,11724,5820,17544,0.0833,0.04166,0.06256,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON2_5ENERO22_05Jan2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "161219,161443,322662,20830,10044,30874,0.1292,0.06223,0.0957,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON4_5ENERO22_05Jan2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "324966,249839,574805,48659,27580,76239,0.1498,0.1104,0.1327,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal2020_raton5_5enero22_05Jan2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "231831,238311,470142,32854,16668,49522,0.1417,0.06995,0.10535,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal2020_raton6_5enero22_05Jan2022_Acq004_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "192847,173222,366069,19405,15373,34778,0.10065,0.08875,0.09503,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_C1_18NOV22_18Nov_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "80218,101984,182202,11379,18380,29759,0.1418,0.1802,0.1633,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_C2_18NOV22_18Nov2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "87066,87865,174931,14948,15009,29957,0.1716,0.1708,0.1713,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_P1_18NOV22_18Nov_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "144517,157388,301905,26254,28996,55250,0.1816,0.1842,0.183,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_P2_16DIC22_16Dec_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "200320,213343,413663,38455,40677,79132,0.192,0.1907,0.1913,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_P3_18NOV22_18Nov_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "138307,143248,281555,24336,26002,50338,0.1759,0.1815,0.1788,62.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_P4_18NOV22_18Nov_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "122908,117692,240600,21622,21291,42913,0.1759,0.1809,0.1783,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_P5_16DIC22_16Dec_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "88885,83935,172820,15397,13346,28743,0.1732,0.159,0.1663,62.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL2020_RATON_P5_18NOV22_18Nov_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "124652,116125,240777,22361,20452,42813,0.1794,0.1761,0.1779,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_2020_raton1D_25jun21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "120459,118300,238759,7380,5600,12980,0.06128,0.04733,0.05435,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_2020_raton1I_25jun21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "115808,116348,232156,7721,7036,14757,0.06665,0.0605,0.06354,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_2020_raton2D_25jun21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "128037,143178,271215,8432,8584,17016,0.06586,0.05997,0.06274,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_2020_raton2I_25jun21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "95822,119363,215185,6601,5815,12416,0.0689,0.0487,0.0577,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_2020_raton_control_25jun21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "91236,88951,180187,6312,4015,10327,0.06915,0.04514,0.0573,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON1_2D1I_10DIC21_10Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "139949,144222,284171,13718,6174,19892,0.098,0.04282,0.07,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON1_2D1I_23DIC21_23Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "164961,150749,315710,17936,6427,24363,0.1087,0.04263,0.07715,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON1_2I1D_26NOV21_26Nov2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "122927,137549,260476,14341,6630,20971,0.11664,0.0482,0.0805,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON2_1D_10DIC21_10Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "149875,147133,297008,14551,7321,21872,0.0971,0.04974,0.07367,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON2_1D_23DIC21_23Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "160176,156533,316709,16213,7279,23492,0.1012,0.0465,0.07416,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON2_1D_26NOV21_26Nov2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "124261,128586,252847,9035,6810,15845,0.0727,0.05295,0.0627,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON3_1I_10DIC21_10Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "154165,151302,305467,10304,9293,19597,0.06683,0.06143,0.06415,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON3_1I_26NOV21_26Nov2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "140270,136597,276867,8612,7754,16366,0.0614,0.05676,0.0591,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON4_2D_10DIC21_10Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "211594,174289,385883,16524,12247,28771,0.07806,0.07025,0.0746,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON4_2D_23DIC21_23Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "244872,202160,447032,28855,19016,47871,0.11786,0.09406,0.1071,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON4_2D_26NOV21_26Nov2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "157777,143745,301522,11344,9674,21018,0.0719,0.0673,0.0697,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON5_2I_10DIC21_10Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "199524,196907,396431,16909,10835,27744,0.0847,0.05502,0.07,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON5_2I_23DIC21_23Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "220068,221916,441984,19201,14426,33627,0.0873,0.065,0.0761,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON5_2I_26NOV21_26Nov2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "188283,175332,363615,18037,8769,26806,0.0958,0.05002,0.0737,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON6_1D1I_10DIC21_10Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "159841,147970,307811,10661,8929,19590,0.0667,0.06033,0.06366,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON6_1D1I_23DIC21_23Dec2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "187045,163428,350473,15880,9097,24977,0.0849,0.05566,0.0713,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON6_1D1I_26NOV21_26Nov2021_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "138664,130088,268752,11734,7350,19084,0.0846,0.0565,0.071,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_1D_13jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "129936,129044,258980,8525,7684,16209,0.0656,0.05954,0.06256,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_1D_28jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "176553,165540,342093,13009,12004,25013,0.07367,0.0725,0.0731,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_1I_13jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "154341,154414,308755,11172,12698,23870,0.0724,0.0822,0.07733,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_2D_13jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "147336,158733,306069,12152,10558,22710,0.08246,0.0665,0.0742,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_2I_13jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "116686,151201,267887,10824,8652,19476,0.0928,0.05722,0.0727,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_C1_30NOV22_30Nov2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "96655,103422,200077,17041,17587,34628,0.1763,0.17,0.1731,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_C2_16DIC22_16Dec2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "99320,99318,198638,17740,16462,34202,0.1786,0.1658,0.1722,62.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_C2_30NOV22_30Nov2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "90520,84155,174675,16384,12935,29319,0.181,0.1537,0.1678,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_control_13jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "99172,104473,203645,2471,10175,12646,0.02492,0.0974,0.0621,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TropismoRenal_raton_control_28jul21\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "132526,110788,243314,11605,4521,16126,0.0876,0.0408,0.0663,65.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P1_16DIC22_16Dec2022_Acq001\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "213975,225840,439815,40466,43483,83949,0.1891,0.1925,0.1909,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P1_2DIC22_02Dec2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "135987,117486,253473,24110,21472,45582,0.1772,0.1827,0.1798,64.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P2_18NOV22_18Nov2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "138084,131052,269136,24976,23167,48143,0.1809,0.1768,0.1788,62.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P2_30NOV22_01Dec2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "166198,141950,308148,29370,24104,53474,0.1768,0.1698,0.1736,62.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P3_30NOV22_01Dec2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "169254,172810,342064,31043,32460,63503,0.1835,0.1879,0.1857,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P4_30NOV22_01Dec2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "145327,112247,257574,26439,19329,45768,0.1819,0.1722,0.1777,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n",
      "TROPISMORENAL_RATON_P5_2DIC22_02Dec2022_Acq001_ring\n",
      "kidneyVolLeft,kidneyVolRight,kidneyVolTotal,diseasedVolLeft,diseasedVolRight,diseasedVolTotal,diseasedPropLeft,diseasedPropRight,diseasedPropTot,diseasedThres,mmVoxSize0,mmVoxSize1,mmVoxSize2\n",
      "156166,145356,301522,28545,26748,55293,0.1827,0.184,0.1833,63.0,0.122,0.122,0.122\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Analyze the results\n",
    "resultFileNames = glob(os.path.dirname(Results_folder)+'/*Results*.txt')\n",
    "\n",
    "numImages = len(resultFileNames)\n",
    "\n",
    "imageNames = list()\n",
    "\n",
    "kidneyVolumes = np.zeros([numImages,2])\n",
    "totalKidneyVolume = np.zeros(numImages)\n",
    "diseasedVolumes = np.zeros([numImages,2])\n",
    "totalDiseasedVolume = np.zeros(numImages)\n",
    "\n",
    "diseasedProp = np.zeros([numImages,2])\n",
    "totalDiseasedProp = np.zeros(numImages)\n",
    "\n",
    "voxelSize = np.zeros([numImages,3])\n",
    "\n",
    "for count,fileName in enumerate(resultFileNames):\n",
    "    nameParts=fileName.split('-Results-')\n",
    "    name=os.path.basename(nameParts[0])\n",
    "    imageNames.append(name)\n",
    "    \n",
    "    segType = nameParts[1]\n",
    "    with open(fileName,'r') as f:\n",
    "        lines = f.readlines()\n",
    "        lines[0]=lines[0].replace('\\n','')\n",
    "        \n",
    "        print(name)\n",
    "        print(lines[0])\n",
    "        print(lines[1])\n",
    "        print('\\n')\n",
    "        \n",
    "        #Obtain the data (second line)\n",
    "        data = lines[1].split(',')\n",
    "        \n",
    "        #Depending on the type of segmentation the data will be ordered in a different way\n",
    "        kidneyVolumes[count,:] = np.array([np.int32(data[0]),np.int32(data[1])])\n",
    "        totalKidneyVolume[count] = np.int32(data[2])    \n",
    "         \n",
    "        diseasedVolumes[count,:] = np.array([np.int32(data[3]),np.int32(data[4])])\n",
    "        totalDiseasedVolume[count] = np.int32(data[5])\n",
    "            \n",
    "        diseasedProp[count,:] = np.array([np.float16(data[6]),np.float16(data[7])])\n",
    "        totalDiseasedProp[count] = np.float16(data[8])\n",
    "        \n",
    "        voxelSize[count,:] = np.array([np.float16(data[9]),np.float16(data[10]),np.float16(data[11])])\n",
    "        \n",
    "\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "37d1f1f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RATON1 5ENERO22\n",
      "RATON2 5ENERO22\n",
      "RATON4 5ENERO22\n",
      "raton5 5enero22\n",
      "raton6 5enero22\n",
      "RATON_C1 18NOV22\n",
      "RATON_C2 18NOV22\n",
      "RATON_P1 18NOV22\n",
      "RATON_P2 16DIC22\n",
      "RATON_P3 18NOV22\n",
      "RATON_P4 18NOV22\n",
      "RATON_P5 16DIC22\n",
      "RATON_P5 18NOV22\n",
      "raton1D 25jun21\n",
      "raton1I 25jun21\n",
      "raton2D 25jun21\n",
      "raton2I 25jun21\n",
      "raton_control 25jun21\n",
      "RATON1_2D1I 10DIC21\n",
      "RATON1_2D1I 23DIC21\n",
      "RATON1_2I1D 26NOV21\n",
      "RATON2_1D 10DIC21\n",
      "RATON2_1D 23DIC21\n",
      "RATON2_1D 26NOV21\n",
      "RATON3_1I 10DIC21\n",
      "RATON3_1I 26NOV21\n",
      "RATON4_2D 10DIC21\n",
      "RATON4_2D 23DIC21\n",
      "RATON4_2D 26NOV21\n",
      "RATON5_2I 10DIC21\n",
      "RATON5_2I 23DIC21\n",
      "RATON5_2I 26NOV21\n",
      "RATON6_1D1I 10DIC21\n",
      "RATON6_1D1I 23DIC21\n",
      "RATON6_1D1I 26NOV21\n",
      "raton_1D 13jul21\n",
      "raton_1D 28jul21\n",
      "raton_1I 13jul21\n",
      "raton_2D 13jul21\n",
      "raton_2I 13jul21\n",
      "RATON_C1 30NOV22\n",
      "RATON_C2 16DIC22\n",
      "RATON_C2 30NOV22\n",
      "raton_control 13jul21\n",
      "raton_control 28jul21\n",
      "RATON_P1 16DIC22\n",
      "RATON_P1 2DIC22\n",
      "RATON_P2 18NOV22\n",
      "RATON_P2 30NOV22\n",
      "RATON_P3 30NOV22\n",
      "RATON_P4 30NOV22\n",
      "RATON_P5 2DIC22\n"
     ]
    }
   ],
   "source": [
    "mouseNames =list()\n",
    "dates = list()\n",
    "\n",
    "\n",
    "#Possible strings to code month names\n",
    "monthNames = list(['jan','ene','feb','mar','apr','may','jun','jul','aug','ago','sep','sept','oct',\\\n",
    "                   'nov','dec','dic'])\n",
    "\n",
    "for path in resultFileNames:\n",
    "    \n",
    "    name = os.path.basename(path).split('-Results-measures(in_voxels).txt')[0]\n",
    "  \n",
    "    details = name.split('_')\n",
    "    \n",
    "    #If the whole string at the second position is not a number, choose it as the initial name\n",
    "    if not details[1].isdigit():\n",
    "       initIndex = 1\n",
    "    \n",
    "    #Else look for the name in the next position\n",
    "    else:\n",
    "        initIndex = 2\n",
    "    \n",
    "    #The initial name will be this one, but it may receive aditional information\n",
    "    mouseName = details[initIndex]\n",
    "        \n",
    "    index = initIndex+1\n",
    "        \n",
    "    #Add as many extra entries to the name as you can before you get to a date\n",
    "    while not any(map(details[index].lower().__contains__, monthNames)):\n",
    "        mouseName = mouseName + '_' + details[index]\n",
    "        index+=1\n",
    "            \n",
    "    #The next entry will correspond to a date\n",
    "    date=details[index]\n",
    "    \n",
    "    #print(details)\n",
    "    print(mouseName, date)\n",
    "    \n",
    "    mouseNames.append(mouseName)\n",
    "    dates.append(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "56638d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kidneyVolumes\n",
      "\n",
      "RATON1 5ENERO22 [140680. 139682.]\n",
      "RATON2 5ENERO22 [161219. 161443.]\n",
      "RATON4 5ENERO22 [324966. 249839.]\n",
      "raton5 5enero22 [231831. 238311.]\n",
      "raton6 5enero22 [192847. 173222.]\n",
      "RATON_C1 18NOV22 [ 80218. 101984.]\n",
      "RATON_C2 18NOV22 [87066. 87865.]\n",
      "RATON_P1 18NOV22 [144517. 157388.]\n",
      "RATON_P2 16DIC22 [200320. 213343.]\n",
      "RATON_P3 18NOV22 [138307. 143248.]\n",
      "RATON_P4 18NOV22 [122908. 117692.]\n",
      "RATON_P5 16DIC22 [88885. 83935.]\n",
      "RATON_P5 18NOV22 [124652. 116125.]\n",
      "raton1D 25jun21 [120459. 118300.]\n",
      "raton1I 25jun21 [115808. 116348.]\n",
      "raton2D 25jun21 [128037. 143178.]\n",
      "raton2I 25jun21 [ 95822. 119363.]\n",
      "raton_control 25jun21 [91236. 88951.]\n",
      "RATON1_2D1I 10DIC21 [139949. 144222.]\n",
      "RATON1_2D1I 23DIC21 [164961. 150749.]\n",
      "RATON1_2I1D 26NOV21 [122927. 137549.]\n",
      "RATON2_1D 10DIC21 [149875. 147133.]\n",
      "RATON2_1D 23DIC21 [160176. 156533.]\n",
      "RATON2_1D 26NOV21 [124261. 128586.]\n",
      "RATON3_1I 10DIC21 [154165. 151302.]\n",
      "RATON3_1I 26NOV21 [140270. 136597.]\n",
      "RATON4_2D 10DIC21 [211594. 174289.]\n",
      "RATON4_2D 23DIC21 [244872. 202160.]\n",
      "RATON4_2D 26NOV21 [157777. 143745.]\n",
      "RATON5_2I 10DIC21 [199524. 196907.]\n",
      "RATON5_2I 23DIC21 [220068. 221916.]\n",
      "RATON5_2I 26NOV21 [188283. 175332.]\n",
      "RATON6_1D1I 10DIC21 [159841. 147970.]\n",
      "RATON6_1D1I 23DIC21 [187045. 163428.]\n",
      "RATON6_1D1I 26NOV21 [138664. 130088.]\n",
      "raton_1D 13jul21 [129936. 129044.]\n",
      "raton_1D 28jul21 [176553. 165540.]\n",
      "raton_1I 13jul21 [154341. 154414.]\n",
      "raton_2D 13jul21 [147336. 158733.]\n",
      "raton_2I 13jul21 [116686. 151201.]\n",
      "RATON_C1 30NOV22 [ 96655. 103422.]\n",
      "RATON_C2 16DIC22 [99320. 99318.]\n",
      "RATON_C2 30NOV22 [90520. 84155.]\n",
      "raton_control 13jul21 [ 99172. 104473.]\n",
      "raton_control 28jul21 [132526. 110788.]\n",
      "RATON_P1 16DIC22 [213975. 225840.]\n",
      "RATON_P1 2DIC22 [135987. 117486.]\n",
      "RATON_P2 18NOV22 [138084. 131052.]\n",
      "RATON_P2 30NOV22 [166198. 141950.]\n",
      "RATON_P3 30NOV22 [169254. 172810.]\n",
      "RATON_P4 30NOV22 [145327. 112247.]\n",
      "RATON_P5 2DIC22 [156166. 145356.]\n",
      "\n",
      "\n",
      "totalKidneyVolume\n",
      "\n",
      "RATON1 5ENERO22 280362.0\n",
      "RATON2 5ENERO22 322662.0\n",
      "RATON4 5ENERO22 574805.0\n",
      "raton5 5enero22 470142.0\n",
      "raton6 5enero22 366069.0\n",
      "RATON_C1 18NOV22 182202.0\n",
      "RATON_C2 18NOV22 174931.0\n",
      "RATON_P1 18NOV22 301905.0\n",
      "RATON_P2 16DIC22 413663.0\n",
      "RATON_P3 18NOV22 281555.0\n",
      "RATON_P4 18NOV22 240600.0\n",
      "RATON_P5 16DIC22 172820.0\n",
      "RATON_P5 18NOV22 240777.0\n",
      "raton1D 25jun21 238759.0\n",
      "raton1I 25jun21 232156.0\n",
      "raton2D 25jun21 271215.0\n",
      "raton2I 25jun21 215185.0\n",
      "raton_control 25jun21 180187.0\n",
      "RATON1_2D1I 10DIC21 284171.0\n",
      "RATON1_2D1I 23DIC21 315710.0\n",
      "RATON1_2I1D 26NOV21 260476.0\n",
      "RATON2_1D 10DIC21 297008.0\n",
      "RATON2_1D 23DIC21 316709.0\n",
      "RATON2_1D 26NOV21 252847.0\n",
      "RATON3_1I 10DIC21 305467.0\n",
      "RATON3_1I 26NOV21 276867.0\n",
      "RATON4_2D 10DIC21 385883.0\n",
      "RATON4_2D 23DIC21 447032.0\n",
      "RATON4_2D 26NOV21 301522.0\n",
      "RATON5_2I 10DIC21 396431.0\n",
      "RATON5_2I 23DIC21 441984.0\n",
      "RATON5_2I 26NOV21 363615.0\n",
      "RATON6_1D1I 10DIC21 307811.0\n",
      "RATON6_1D1I 23DIC21 350473.0\n",
      "RATON6_1D1I 26NOV21 268752.0\n",
      "raton_1D 13jul21 258980.0\n",
      "raton_1D 28jul21 342093.0\n",
      "raton_1I 13jul21 308755.0\n",
      "raton_2D 13jul21 306069.0\n",
      "raton_2I 13jul21 267887.0\n",
      "RATON_C1 30NOV22 200077.0\n",
      "RATON_C2 16DIC22 198638.0\n",
      "RATON_C2 30NOV22 174675.0\n",
      "raton_control 13jul21 203645.0\n",
      "raton_control 28jul21 243314.0\n",
      "RATON_P1 16DIC22 439815.0\n",
      "RATON_P1 2DIC22 253473.0\n",
      "RATON_P2 18NOV22 269136.0\n",
      "RATON_P2 30NOV22 308148.0\n",
      "RATON_P3 30NOV22 342064.0\n",
      "RATON_P4 30NOV22 257574.0\n",
      "RATON_P5 2DIC22 301522.0\n",
      "\n",
      "\n",
      "diseasedVolumes\n",
      "\n",
      "RATON1 5ENERO22 [11724.  5820.]\n",
      "RATON2 5ENERO22 [20830. 10044.]\n",
      "RATON4 5ENERO22 [48659. 27580.]\n",
      "raton5 5enero22 [32854. 16668.]\n",
      "raton6 5enero22 [19405. 15373.]\n",
      "RATON_C1 18NOV22 [11379. 18380.]\n",
      "RATON_C2 18NOV22 [14948. 15009.]\n",
      "RATON_P1 18NOV22 [26254. 28996.]\n",
      "RATON_P2 16DIC22 [38455. 40677.]\n",
      "RATON_P3 18NOV22 [24336. 26002.]\n",
      "RATON_P4 18NOV22 [21622. 21291.]\n",
      "RATON_P5 16DIC22 [15397. 13346.]\n",
      "RATON_P5 18NOV22 [22361. 20452.]\n",
      "raton1D 25jun21 [7380. 5600.]\n",
      "raton1I 25jun21 [7721. 7036.]\n",
      "raton2D 25jun21 [8432. 8584.]\n",
      "raton2I 25jun21 [6601. 5815.]\n",
      "raton_control 25jun21 [6312. 4015.]\n",
      "RATON1_2D1I 10DIC21 [13718.  6174.]\n",
      "RATON1_2D1I 23DIC21 [17936.  6427.]\n",
      "RATON1_2I1D 26NOV21 [14341.  6630.]\n",
      "RATON2_1D 10DIC21 [14551.  7321.]\n",
      "RATON2_1D 23DIC21 [16213.  7279.]\n",
      "RATON2_1D 26NOV21 [9035. 6810.]\n",
      "RATON3_1I 10DIC21 [10304.  9293.]\n",
      "RATON3_1I 26NOV21 [8612. 7754.]\n",
      "RATON4_2D 10DIC21 [16524. 12247.]\n",
      "RATON4_2D 23DIC21 [28855. 19016.]\n",
      "RATON4_2D 26NOV21 [11344.  9674.]\n",
      "RATON5_2I 10DIC21 [16909. 10835.]\n",
      "RATON5_2I 23DIC21 [19201. 14426.]\n",
      "RATON5_2I 26NOV21 [18037.  8769.]\n",
      "RATON6_1D1I 10DIC21 [10661.  8929.]\n",
      "RATON6_1D1I 23DIC21 [15880.  9097.]\n",
      "RATON6_1D1I 26NOV21 [11734.  7350.]\n",
      "raton_1D 13jul21 [8525. 7684.]\n",
      "raton_1D 28jul21 [13009. 12004.]\n",
      "raton_1I 13jul21 [11172. 12698.]\n",
      "raton_2D 13jul21 [12152. 10558.]\n",
      "raton_2I 13jul21 [10824.  8652.]\n",
      "RATON_C1 30NOV22 [17041. 17587.]\n",
      "RATON_C2 16DIC22 [17740. 16462.]\n",
      "RATON_C2 30NOV22 [16384. 12935.]\n",
      "raton_control 13jul21 [ 2471. 10175.]\n",
      "raton_control 28jul21 [11605.  4521.]\n",
      "RATON_P1 16DIC22 [40466. 43483.]\n",
      "RATON_P1 2DIC22 [24110. 21472.]\n",
      "RATON_P2 18NOV22 [24976. 23167.]\n",
      "RATON_P2 30NOV22 [29370. 24104.]\n",
      "RATON_P3 30NOV22 [31043. 32460.]\n",
      "RATON_P4 30NOV22 [26439. 19329.]\n",
      "RATON_P5 2DIC22 [28545. 26748.]\n",
      "\n",
      "\n",
      "totalDiseasedVolume\n",
      "\n",
      "RATON1 5ENERO22 17544.0\n",
      "RATON2 5ENERO22 30874.0\n",
      "RATON4 5ENERO22 76239.0\n",
      "raton5 5enero22 49522.0\n",
      "raton6 5enero22 34778.0\n",
      "RATON_C1 18NOV22 29759.0\n",
      "RATON_C2 18NOV22 29957.0\n",
      "RATON_P1 18NOV22 55250.0\n",
      "RATON_P2 16DIC22 79132.0\n",
      "RATON_P3 18NOV22 50338.0\n",
      "RATON_P4 18NOV22 42913.0\n",
      "RATON_P5 16DIC22 28743.0\n",
      "RATON_P5 18NOV22 42813.0\n",
      "raton1D 25jun21 12980.0\n",
      "raton1I 25jun21 14757.0\n",
      "raton2D 25jun21 17016.0\n",
      "raton2I 25jun21 12416.0\n",
      "raton_control 25jun21 10327.0\n",
      "RATON1_2D1I 10DIC21 19892.0\n",
      "RATON1_2D1I 23DIC21 24363.0\n",
      "RATON1_2I1D 26NOV21 20971.0\n",
      "RATON2_1D 10DIC21 21872.0\n",
      "RATON2_1D 23DIC21 23492.0\n",
      "RATON2_1D 26NOV21 15845.0\n",
      "RATON3_1I 10DIC21 19597.0\n",
      "RATON3_1I 26NOV21 16366.0\n",
      "RATON4_2D 10DIC21 28771.0\n",
      "RATON4_2D 23DIC21 47871.0\n",
      "RATON4_2D 26NOV21 21018.0\n",
      "RATON5_2I 10DIC21 27744.0\n",
      "RATON5_2I 23DIC21 33627.0\n",
      "RATON5_2I 26NOV21 26806.0\n",
      "RATON6_1D1I 10DIC21 19590.0\n",
      "RATON6_1D1I 23DIC21 24977.0\n",
      "RATON6_1D1I 26NOV21 19084.0\n",
      "raton_1D 13jul21 16209.0\n",
      "raton_1D 28jul21 25013.0\n",
      "raton_1I 13jul21 23870.0\n",
      "raton_2D 13jul21 22710.0\n",
      "raton_2I 13jul21 19476.0\n",
      "RATON_C1 30NOV22 34628.0\n",
      "RATON_C2 16DIC22 34202.0\n",
      "RATON_C2 30NOV22 29319.0\n",
      "raton_control 13jul21 12646.0\n",
      "raton_control 28jul21 16126.0\n",
      "RATON_P1 16DIC22 83949.0\n",
      "RATON_P1 2DIC22 45582.0\n",
      "RATON_P2 18NOV22 48143.0\n",
      "RATON_P2 30NOV22 53474.0\n",
      "RATON_P3 30NOV22 63503.0\n",
      "RATON_P4 30NOV22 45768.0\n",
      "RATON_P5 2DIC22 55293.0\n",
      "\n",
      "\n",
      "diseasedProp\n",
      "\n",
      "RATON1 5ENERO22 [0.08331299 0.04165649]\n",
      "RATON2 5ENERO22 [0.12915039 0.06222534]\n",
      "RATON4 5ENERO22 [0.14978027 0.1104126 ]\n",
      "raton5 5enero22 [0.14172363 0.06994629]\n",
      "raton6 5enero22 [0.10064697 0.08874512]\n",
      "RATON_C1 18NOV22 [0.1418457  0.18017578]\n",
      "RATON_C2 18NOV22 [0.17163086 0.17077637]\n",
      "RATON_P1 18NOV22 [0.18164062 0.1842041 ]\n",
      "RATON_P2 16DIC22 [0.1920166  0.19067383]\n",
      "RATON_P3 18NOV22 [0.17590332 0.18151855]\n",
      "RATON_P4 18NOV22 [0.17590332 0.1809082 ]\n",
      "RATON_P5 16DIC22 [0.17321777 0.15905762]\n",
      "RATON_P5 18NOV22 [0.17944336 0.17614746]\n",
      "raton1D 25jun21 [0.0612793  0.04733276]\n",
      "raton1I 25jun21 [0.06665039 0.06048584]\n",
      "raton2D 25jun21 [0.06585693 0.05996704]\n",
      "raton2I 25jun21 [0.06890869 0.04870605]\n",
      "raton_control 25jun21 [0.06915283 0.0451355 ]\n",
      "RATON1_2D1I 10DIC21 [0.09802246 0.04281616]\n",
      "RATON1_2D1I 23DIC21 [0.10870361 0.04263306]\n",
      "RATON1_2I1D 26NOV21 [0.11663818 0.04818726]\n",
      "RATON2_1D 10DIC21 [0.09710693 0.04974365]\n",
      "RATON2_1D 23DIC21 [0.10119629 0.04650879]\n",
      "RATON2_1D 26NOV21 [0.07269287 0.052948  ]\n",
      "RATON3_1I 10DIC21 [0.0668335  0.06143188]\n",
      "RATON3_1I 26NOV21 [0.06140137 0.0567627 ]\n",
      "RATON4_2D 10DIC21 [0.07806396 0.07025146]\n",
      "RATON4_2D 23DIC21 [0.11785889 0.09405518]\n",
      "RATON4_2D 26NOV21 [0.07189941 0.06732178]\n",
      "RATON5_2I 10DIC21 [0.0847168  0.05502319]\n",
      "RATON5_2I 23DIC21 [0.08728027 0.06500244]\n",
      "RATON5_2I 26NOV21 [0.0958252  0.05001831]\n",
      "RATON6_1D1I 10DIC21 [0.06671143 0.06033325]\n",
      "RATON6_1D1I 23DIC21 [0.0848999  0.05566406]\n",
      "RATON6_1D1I 26NOV21 [0.08459473 0.05648804]\n",
      "raton_1D 13jul21 [0.06561279 0.05953979]\n",
      "raton_1D 28jul21 [0.07366943 0.07250977]\n",
      "raton_1I 13jul21 [0.0723877  0.08221436]\n",
      "raton_2D 13jul21 [0.0824585  0.06652832]\n",
      "raton_2I 13jul21 [0.09277344 0.05722046]\n",
      "RATON_C1 30NOV22 [0.17626953 0.17004395]\n",
      "RATON_C2 16DIC22 [0.17858887 0.16577148]\n",
      "RATON_C2 30NOV22 [0.18103027 0.15368652]\n",
      "raton_control 13jul21 [0.0249176  0.09741211]\n",
      "raton_control 28jul21 [0.08758545 0.040802  ]\n",
      "RATON_P1 16DIC22 [0.18908691 0.19250488]\n",
      "RATON_P1 2DIC22 [0.17724609 0.18273926]\n",
      "RATON_P2 18NOV22 [0.1809082  0.17675781]\n",
      "RATON_P2 30NOV22 [0.17675781 0.1697998 ]\n",
      "RATON_P3 30NOV22 [0.18347168 0.18786621]\n",
      "RATON_P4 30NOV22 [0.18188477 0.17224121]\n",
      "RATON_P5 2DIC22 [0.18273926 0.18395996]\n",
      "\n",
      "\n",
      "totalDiseasedProp\n",
      "RATON1 5ENERO22 0.06256103515625\n",
      "RATON2 5ENERO22 0.095703125\n",
      "RATON4 5ENERO22 0.1326904296875\n",
      "raton5 5enero22 0.1053466796875\n",
      "raton6 5enero22 0.09503173828125\n",
      "RATON_C1 18NOV22 0.163330078125\n",
      "RATON_C2 18NOV22 0.1712646484375\n",
      "RATON_P1 18NOV22 0.1829833984375\n",
      "RATON_P2 16DIC22 0.1912841796875\n",
      "RATON_P3 18NOV22 0.1788330078125\n",
      "RATON_P4 18NOV22 0.1783447265625\n",
      "RATON_P5 16DIC22 0.166259765625\n",
      "RATON_P5 18NOV22 0.1778564453125\n",
      "raton1D 25jun21 0.054351806640625\n",
      "raton1I 25jun21 0.06353759765625\n",
      "raton2D 25jun21 0.062744140625\n",
      "raton2I 25jun21 0.057708740234375\n",
      "raton_control 25jun21 0.05731201171875\n",
      "RATON1_2D1I 10DIC21 0.07000732421875\n",
      "RATON1_2D1I 23DIC21 0.0771484375\n",
      "RATON1_2I1D 26NOV21 0.08050537109375\n",
      "RATON2_1D 10DIC21 0.07366943359375\n",
      "RATON2_1D 23DIC21 0.07415771484375\n",
      "RATON2_1D 26NOV21 0.06268310546875\n",
      "RATON3_1I 10DIC21 0.06414794921875\n",
      "RATON3_1I 26NOV21 0.059112548828125\n",
      "RATON4_2D 10DIC21 0.0745849609375\n",
      "RATON4_2D 23DIC21 0.10711669921875\n",
      "RATON4_2D 26NOV21 0.0697021484375\n",
      "RATON5_2I 10DIC21 0.07000732421875\n",
      "RATON5_2I 23DIC21 0.07611083984375\n",
      "RATON5_2I 26NOV21 0.07373046875\n",
      "RATON6_1D1I 10DIC21 0.06365966796875\n",
      "RATON6_1D1I 23DIC21 0.0712890625\n",
      "RATON6_1D1I 26NOV21 0.07098388671875\n",
      "raton_1D 13jul21 0.06256103515625\n",
      "raton_1D 28jul21 0.0731201171875\n",
      "raton_1I 13jul21 0.07733154296875\n",
      "raton_2D 13jul21 0.07421875\n",
      "raton_2I 13jul21 0.07269287109375\n",
      "RATON_C1 30NOV22 0.173095703125\n",
      "RATON_C2 16DIC22 0.1722412109375\n",
      "RATON_C2 30NOV22 0.1678466796875\n",
      "raton_control 13jul21 0.062103271484375\n",
      "raton_control 28jul21 0.0662841796875\n",
      "RATON_P1 16DIC22 0.19091796875\n",
      "RATON_P1 2DIC22 0.1798095703125\n",
      "RATON_P2 18NOV22 0.1788330078125\n",
      "RATON_P2 30NOV22 0.173583984375\n",
      "RATON_P3 30NOV22 0.1856689453125\n",
      "RATON_P4 30NOV22 0.177734375\n",
      "RATON_P5 2DIC22 0.183349609375\n"
     ]
    }
   ],
   "source": [
    "numMice = len(kidneyVolumes)\n",
    "print('kidneyVolumes\\n')\n",
    "for i in range(numMice):\n",
    "    print(mouseNames[i],dates[i],kidneyVolumes[i])\n",
    "\n",
    "print('\\n\\ntotalKidneyVolume\\n')\n",
    "for i in range(numMice):\n",
    "    print(mouseNames[i],dates[i],totalKidneyVolume[i])\n",
    "\n",
    "print('\\n\\ndiseasedVolumes\\n')\n",
    "for i in range(numMice):\n",
    "    print(mouseNames[i],dates[i],diseasedVolumes[i])\n",
    "\n",
    "print('\\n\\ntotalDiseasedVolume\\n')\n",
    "for i in range(numMice):\n",
    "    print(mouseNames[i],dates[i],totalDiseasedVolume[i])\n",
    "\n",
    "print('\\n\\ndiseasedProp\\n')\n",
    "for i in range(numMice):\n",
    "    print(mouseNames[i],dates[i],diseasedProp[i])\n",
    "    \n",
    "print('\\n\\ntotalDiseasedProp')\n",
    "for i in range(numMice):\n",
    "    print(mouseNames[i],dates[i],totalDiseasedProp[i])\n",
    "\n"
   ]
  }
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