This script performs Whole Slide Image (WSI) preprocessing, including masking, tiling, normalization, quality checks, encoding and optional whole slide reconstruction after tiling. It can be used as a customazible pipeline for mass WSI processing or to directly call functions to perform specific tasks. As a pipeline, it is designed to ensure that if stopped for any reason, you will be able to continue at the last step that was completed. It also includes both an error report (for any error that may occur and the location it occurred) and a summary report with statistics like % of tissue and time taken to process file.

Masking

Normalization

Tiling

reports

summary report

error report

Requirements

In order to use this package, you must have python (>3.9) installed on your system. Conda is also recommended for installation.

#Installation

First clone the repository and cd into repository:

git clone https://github.com/lolmomarchal/SlideLab.git
cd SlideLab

To install required dependencies you can do through conda:

conda

conda env create -f environment.yml
conda activate slidelab

Usage

Arguments

Input/Output (Required)

Argument	Description	Default
-i, --input_path	Path to the input WSI file	None
-o, --output_path	Path to save the output tiles	None

Tile Customization

Argument	Description	Default
-s, --desired_size	Desired size of the tiles (in pixels)	256
-m, --desired_magnification	Desired magnification level (ex: 20x	20
-ov, --overlap	Factor of overlap between tiles	1 (no overlap)

Overlap example: With a size of 256 and an overlap of 2, tiles would overlap by 128 pixels.

Preprocessing options

Argument	Description	Default
-rb, --remove_blurry_tiles	Remove blurry tiles using a Laplacian filter	False
-n, --normalize_staining	Normalize staining of the tiles	False
-e, --encode	Encode tiles into an .h5 file	False
--extract_high_quality	Extract features for high quality heatmaps	False
--augmentations	Get various augmentations for encoded tiles for model training	0

Thresholds

Argument	Description	Default
-th, --tissue_threshold	Minimum tissue content to consider a tile valid	0.7
-bh, --blur_threshold	Threshold for Laplacian filter variance (blur detection)	0.015
--red_pen_check	Sanity check for % of red pen detected. If above threshold, red_pen mask will be ignored	0.4
--blue_pen_check	Sanity check for % of red pen detected. If above threshold, blue_pen mask will be ignored	0.4

Devices and Multiprocessing

Argument	Description	Default
--device	Specify device (e.g., GPU/CPU)	None (will utilize gpu if available)
--cpu_processes	Number of CPU processes to use	os.cpu_count()
--batch_size	Number of CPU processes to use	16. If using augmentations batch_size will be recalculated by using # of augmentations/batch size

Additional Params

Argument	Description	Default
--min_tiles	Minimum number of valid tiles for a sample to be counted as "valid". Will create additional filtered sample metadata file.	0

Example command

python SlidePreprocessing.py -i /path/to/input/-o /path/to/output/ \
  -s 512 -m 40 --remove_blurry_tiles --normalize_staining --encode \
  -th 0.8 -bh 0.02 --device cuda --batch_size 256

To run individual classes:

Can create related objects and use their associated methods. Please see Example.ipynb and Masking Examples.ipynb.