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Create a conda env (Python \u003e= 3.9)\r\n\r\n#### 1. Install DLWMLS (Required dependency)\r\n```bash\r\ngit clone https://github.com/CBICA/DLWMLS.git\r\ncd DLWMLS\r\npip install -e .\r\n```\r\n\r\n### 1. Install wmh_seg (Optional) \r\n```bash\r\ngit clone https://github.com/euroso97/wmh_seg.git\r\ncd wmh_seg\r\nwget https://huggingface.co/jil202/wmh_seg/resolve/main/ChallengeMatched_Unet_mit_b5.pth\r\n\r\n```\r\n\r\n#### 2. Install NiChart_DLWMLS\r\n```bash\r\ngit clone https://github.com/CBICA/NiChart_DLWMLS.git\r\ncd NiChart_DLWMLS\r\npip install -e .\r\n```\r\n\r\n## Usage\r\n\r\n### Prerequisits:\r\n- T1 image (.nii.gz)\r\n- FL image (.nii.gz)\r\n- DLMUSE mask (.nii.gz) (refer to: [NiChart_DLMUSE](https://github.com/CBICA/NiChart_DLMUSE))\r\n- (*optional) your own WMH mask (.nii.gz)\r\n\r\n#### Required arguments:\r\n\r\n    [-fl, --fl_dir] : Name of the input folder with FL scans  (REQUIRED)\r\n    [-o, --out_dir] : Name of the output folder for segmentation (REQUIRED)\r\n    [--list]        List of MRIDs; first raw (column header) skipped (OPTIONAL)\r\n    [--t1_dir]      Name of the input folder with T1 scans  (OPTIONAL)\r\n    [--t1_suff]     Suffix of the input T1 scans (OPTIONAL, DEFAULT: _T1.nii.gz)\r\n    [--dlmuse_dir]  Name of the input folder with T1 scans  (OPTIONAL)\r\n    [--dlmuse_suff] Suffix of the input T1 scans (OPTIONAL, DEFAULT: _T1_LPS_DLMUSE.nii.gz)\r\n\r\n#### Optional arguments:\r\n\r\n    [-d, --device]  Device to run segmentation ('cuda' (GPU), 'cpu' (CPU) or \r\n                    'mps' (Apple M-series chips supporting 3D CNN))\r\n    [-h, --help]    Show this help message and exit.\r\n    [-V, --version] Show program's version number and exit.\r\n    \r\n#### EXAMPLE USAGE:\r\n\r\n    Executing the full pipeline including seperating WMLS mask into Brain ROI level \r\n        based on the input DLMUSE masks:\r\n\r\n    NiChart_DLWMLS  --list          /path/to/mrid_list.csv \\\r\n                    --fl_dir        /path/to/flair_images  \\\r\n                    --fl_suff       _FL_LPS.nii.gz         \\\r\n                    --t1_dir        /path/to/t1_images     \\\r\n                    --t1_suff       _T1_LPS.nii.gz         \\\r\n                    --dlmuse_dir    /path/to/dlmuse_masks  \\\r\n                    --dlmuse_suff   _T1_LPS_DLMUSE.nii.gz  \\\r\n                    --out_dir       /path/to/output\r\n\r\n\r\n    Using your own WMH masks (skipping DLWMLS segmentation):\r\n\r\n        NiChart_DLWMLS  --list          /path/to/mrid_list.csv \\\r\n                        --fl_dir        /path/to/flair_images  \\\r\n                        --fl_suff       _FL.nii.gz             \\\r\n                        --wmh_dir       /path/to/dlwmls_masks  \\\r\n                        --wmh_suff      _FL_LPS_DLWMLS.nii.gz  \\\r\n                        --t1_dir        /path/to/t1_images     \\\r\n                        --t1_suff       _T1.nii.gz             \\\r\n                        --dlmuse_dir    /path/to/dlmuse_masks  \\\r\n                        --dlmuse_suff   _T1_LPS_DLMUSE.nii.gz  \\\r\n                        --out_dir       /path/to/output        \\\r\n                        --remove_intermediate True             \\\r\n                        --device cpu/cuda\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbica%2Fnichart_dlwmls","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcbica%2Fnichart_dlwmls","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcbica%2Fnichart_dlwmls/lists"}