Using nnU-Net and distance-penalized loss functions to auto-segment OARs in cervical cancer brachytherapy
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Create ‘Taskxx_gyn’ folder under ‘nnUNetFrame/DATASET/nnUNet_raw/nnUNet_raw_data’
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Create 'Taskxx_gyn/imagesTs' folder and put the files you want to infer in the folder
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Rename files as ended with ‘_0000’ using
nnUNet_convert_decathlon_task -i [path of ‘Taskxx_gyn’]
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Step 1: Run create-distance-map\calculate_distance_map_newnorm_step1_USE.py
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Step 2: Run create-distance-map\calculate_distance_map_weighted_step2_USE.py
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Move the files in Step 2 into '/imageTs' folder
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Remove HR-CTV from labels: Run create-distance-map\remove_label_class.py
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