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

You are highly welcomed to use this pre-processing pipeline in your own work for evaluating few-shot medical image segmentation in future. Please consider citing our paper (as well as the original sources of data) if you find this pipeline useful. Thanks!

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 vanilla PANet (ICCV'19) by Kaixin Wang et al. The data augmentation tools are from Dr. Jo Schlemper. Should you have any further questions, please let us know. Thanks again for your interest.