PyTorch implementation of the paper "Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition"

set the nnUNet environmental variable

export nnUNet_raw=/path/to/raw_data
export nnUNet_preprocessed=/path/to/preprocessed_data
export nnUNet_results=/path/to/result

1. Retina Fluid Segmentation

1. download the dataset from https://retouch.grand-challenge.org/Download/

2. run python nnunetv2/run/run_training.py dataset_id configuration fold -tr RetinaTrainer --no-debug for training and testing. Please refer to nnUNet for details about dataset_id, configuration and fold.

2. BraTS Segmentation

1. download the datast from Google Drive
2. run python nnunetv2/run/run_training.py dataset_id configuration fold -tr BrainTrainer --no-debug for training and testing.

3. LiTS Segmentation

1. download the datast from Google Drive
2. run python nnunetv2/run/run_training.py dataset_id configuration fold -tr LiverTrainer --no-debug for training and testing.