GLaMur: A Gated Linear Attentive Multiscale Residual U-Net for 3D Medical Image Segmentation

Network Design

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Installation

The code is tested with PyTorch 1.11.0 and CUDA 11.3. After cloning the repository, follow the below steps for installation,

1. Install PyTorch and torchvision

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113

2. Install other dependencies

pip install -r requirements.txt

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Dataset

We follow the same dataset preprocessing as in UNETR++. We conducted extensive experiments on five benchmarks: Synapse, BTCV, ACDC, and Decathlon-Lung.

Please refer to Setting up the datasets on nnFormer repository for more details.

Training

The following scripts can be used for training our UNETR++ model on the datasets:

bash training_scripts/run_training_synapse.sh
bash training_scripts/run_training_acdc.sh
bash training_scripts/run_training_lung.sh
bash training_scripts/run_training_tumor.sh

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Evaluation

The checkpoints are avilable here [!(https://drive.google.com/drive/folders/1D_yXZGsHCjAWLHMMnQKAmtKpefv-dzx3?usp=sharing)]

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Acknowledgement

This repository is built based on nnFormer repository.