Abdominal Organ Segmentations of UK Biobank (UKBB) and German National Cohort (GNC) Studies

Getting Started

Abdominal organ segmentations for UKBB and GNC studies can be obtained with 4 straightforward steps below.

Obtaining Predictions

Arguments

Folder names can be set as preferred. Details of arguments that will be used in the script are as follows:

zip_folder        = Folder that contains downloaded zip files for each subject. 
nifti_folder      = Folder that contains subjects with stitched volumes as .nii.gz files
nnunet_folder     = Folder that contains subjects formatted for nnUNet
prediction_folder = Folder that contains final predictions
output_folder     = Folder that contains predictions with the original naming
dataset_name      = Either ukbb or gnc
num_channels      = Either 1 or 4

Step 0: Download data

Download and put whole-body MRI data into a single directory

Step 1: Run extract_ukbb.py or extract_gnc.py

This is the initial pre-processing step (only run either UKBB or GNC script depending on your source of data).

python extract_ukbb.py 
    --zip_folder my_original_data/ 
    --nifti_folder my_nifti_data/

Step 2: Run convert2nnunet.py

The script converts files to the nnUNet naming.

python convert2nnunet.py 
    --nifti_folder my_nifti_data/ 
    --nnunet_folder my_nnunet_data/ 
    --dataset_name ukbb 
    --num_channels 4

Step 3: Run predict.py

The script generates the predictions for abdominal organs (example below is for UKBB with 4-channel model).

export CUDA_VISIBLE_DEVICES=0 
export RESULTS_FOLDER=models/ 
python predict.py 
    --nnunet_folder my_nnunet_data/ 
    --prediction_folder my_predictions/ 
    --dataset_name ukbb 
    --num_channels 4

Step 4: Run convert2original.py

The script converts predictions back to the original naming.

python convert2original.py 
    --prediction_folder my_predictions/ 
    --output_folder my_outputs/

All organ segmentations are saved into the output folder with their original naming convention.