# monai_wholeBody_ct_segmentation You can run code with your own PC/Notebook/... or on Google Colab. Open In Colab ## Create a virtual environment ``` conda create -n [NAME] python==3.9 ``` ## Start the environment ``` conda activate [NAME] ``` ## Clone Repository ``` git clone https://github.com/ytl0623/monai_wholeBody_ct_segmentation.git ``` ## Go to the cloned folder ``` cd monai_wholeBody_ct_segmentation ``` ## Install the dependencies ``` pip install -r requirements.txt ``` ## Execute inference It will cost about three minutes. Check NIFTI directory after run done. ``` python -m monai.bundle run --config_file configs/inference.json ``` ## Unzip inference file ``` gzip -d NIFTI/DLCSI033/DLCSI033_trans.nii.gz ``` ## Convert NIFTI file to mask file It will cost about three minutes. Check MONAI directory after run done. ``` python nii2png.py ``` ## Generate DICOM-RT file It will cost about two minutes. Check DICOM directory after run done. ``` python main.py ``` ## Download DICOM directory There are two DICOM-RT files. (Original and MONAI) ![Download DICOM directory](https://github.com/ytl0623/monai_wholeBody_ct_segmentation/assets/55120101/3a606842-88c0-4253-9072-0c5c7e2d89ee) ## Download [Dicompyler](https://github.com/bastula/dicompyler/releases/download/release-0.4.2/dicompyler_setup-0.4.2.win32.exe) ![Download Dicompyler](https://github.com/ytl0623/monai_wholeBody_ct_segmentation/assets/55120101/f39cea95-7d57-46a8-a707-db328cf8be0d) ## Show results with Dicompyler Pay attention to the Chinese path. ![Show results with Dicompyler](https://github.com/ytl0623/monai_wholeBody_ct_segmentation/assets/55120101/9c8714fd-b28a-4493-895d-28ec621c1047) ## Reference - https://github.com/Kiragroh/Kira_DICOM-RT-Anonymizer-MG - https://github.com/rordenlab/dcm2niix - https://github.com/Project-MONAI/model-zoo - https://monai.io/model-zoo.html