https://github.com/bhklab/brachynewloss
https://github.com/bhklab/brachynewloss
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/bhklab/brachynewloss
- Owner: bhklab
- License: mit
- Created: 2023-11-22T23:03:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-19T13:34:56.000Z (about 1 year ago)
- Last Synced: 2024-12-30T03:21:07.216Z (5 months ago)
- Language: Python
- Size: 686 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Near-to-Target-aware OARs Segmentation in Cervical HDR Brachytherapy via Deep Learning
Using nnU-Net and distance-penalized loss functions to auto-segment OARs in cervical cancer brachytherapy
## nnU-Net preprocess
1. Create ‘Taskxx_gyn’ folder under ‘nnUNetFrame/DATASET/nnUNet_raw/nnUNet_raw_data’
2. Create 'Taskxx_gyn/imagesTs' folder and put the files you want to infer in the folder
3. Rename files as ended with ‘_0000’ using
```
nnUNet_convert_decathlon_task -i [path of ‘Taskxx_gyn’]
```
## Create distance map
1. Step 1: Run create-distance-map\calculate_distance_map_newnorm_step1_USE.py
2. Step 2: Run create-distance-map\calculate_distance_map_weighted_step2_USE.py
3. Move the files in Step 2 into '/imageTs' folder
4. Remove HR-CTV from labels: Run create-distance-map\remove_label_class.py
## Inference
```
nnUNet_predict -i [imagesTs] -o [inference folder] -t [task number] -m 3d_fullres -tr nnUNetTrainerV2_OAR_distDAv2mirror_noDS_DPCE -f all -p nnUNetPlansv2.1_ch1
```