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https://github.com/BioMedIA/UKBB-GNC-Abdominal-Segmentation
Abdominal Organ Segmentation of UK Biobank and GNC Studies
https://github.com/BioMedIA/UKBB-GNC-Abdominal-Segmentation
Last synced: 2 days ago
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Abdominal Organ Segmentation of UK Biobank and GNC Studies
- Host: GitHub
- URL: https://github.com/BioMedIA/UKBB-GNC-Abdominal-Segmentation
- Owner: BioMedIA
- License: apache-2.0
- Created: 2020-11-20T17:46:48.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-06-19T23:49:46.000Z (over 1 year ago)
- Last Synced: 2024-08-02T16:46:10.536Z (3 months ago)
- Language: Python
- Homepage:
- Size: 26.4 KB
- Stars: 2
- Watchers: 2
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-uk-biobank - UKBB-GNC-Abdominal-Segmentation - based abdominal organ segmentation in UK Biobank | (Imaging phenotypes / Abdominal MRI)
README
# Abdominal Organ Segmentations of UK Biobank (UKBB) and German National Cohort (GNC) Studies
If you utilize this repository in your projects, please consider citing our work:
```
[1] Kart, T., Fischer, M., et. al. Deep Learning‐Based Automated Abdominal Organ Segmentation
in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies. Investigative Radiology
56(6):p 401-408, June 2021. doi: 10.1097/RLI.0000000000000755
[2] Kart, T., et al. Automated imaging-based abdominal organ segmentation and quality control
in 20,000 participants of the UK Biobank and German National Cohort Studies. Sci Rep 12, 18733 (2022).
doi: 10.1038/s41598-022-23632-9
[3] Gatidis S, Kart T, et.al. Better Together: Data Harmonization and Cross-Study Analysis of
Abdominal MRI Data From UK Biobank and the German National Cohort. Invest Radiol. 2023 May 1;58(5):346-354.
doi: 10.1097/RLI.0000000000000941.
```
## Getting Started
First, check dependencies in requirements.txt and make sure they are installed in your environment. Then, abdominal organ segmentations for the UKBB and GNC studies can be obtained with 4 straightforward steps detailed below.
### Requirements
If you want to install them, simply run the following:
```
pip install -r requirements.txt
```
## 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. (Note: you can put 1st and 2nd visits of a subject in the same 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/
```
The script takes into account of multiple visits in the UK Biobank. Therefore, the ```my_nifti_data``` directory may include different visits of the same subject as separate subdirectories. We follow the UK Biobank's naming convention so these subdirectories will be named as "SubjectID_VisitID".
### 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).
```
CUDA_VISIBLE_DEVICES=0
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.
### Maintainer: Turkay Kart
If you have any questions, please reach me by email or Twitter:
Email: [email protected]\
Twitter: [@turkaykart](https://twitter.com/turkaykart)