SRPNet

1. Dependencies

Please install essential dependencies (see requirements.txt)

dcm2nii
json5==0.8.5
jupyter==1.0.0
nibabel==2.5.1
numpy==1.15.1
opencv-python==4.1.1.26
Pillow==7.1.0 
sacred==0.7.5
scikit-image==0.14.0
SimpleITK==1.2.3
torch==1.3.0
torchvision==0.4.1

2. Data pre-processing

Abdominal MRI

0. Download Combined Healthy Abdominal Organ Segmentation dataset and put the /MR folder under ./data/CHAOST2/ directory

1. Converting downloaded data (T2 fold) to nii files in 3D for the ease of reading

run ./data/CHAOST2/dcm_img_to_nii.sh to convert dicom images to nifti files.

run ./data/CHAOST2/png_gth_to_nii.ipynp to convert ground truth with png format to nifti.

2. Pre-processing downloaded images

run ./data/CHAOST2/image_normalize.ipynb

Abdominal CT

0. Download Synapse Multi-atlas Abdominal Segmentation dataset and put the /img and /label folders under ./data/SABS/ directory

1. Intensity windowing

run ./data/SABS/intensity_normalization.ipynb to apply abdominal window.

2. Crop irrelavent emptry background and resample images

run ./data/SABS/resampling_and_roi.ipynb

Shared steps

3. Build class-slice indexing for setting up experiments

run ./data/<CHAOST2/SABS>class_slice_index_gen.ipynb

3. Pseudolabel generation

run ./data_preprocessing/pseudolabel_gen.ipynb. You might need to specify which dataset to use within the notebook.

4. Running training and evaluation

run ./examples/train_ssl_abdominal_<mri/ct>.sh and ./examples/test_ssl_abdominal_<mri/ct>.sh

Acknowledgement

This code is based on SSL-ALPNet(ECCV'20) by Ouyang et al.