NiChart_DLWMLS

Run Deep-Learning-based-White-Matter-Lesion-Segmentation on your data (requires FLAIR, optional T1 masks for granular segmentation).

Executing the full pipeline including seperating WMLS mask into Brain ROI level based on the input DLMUSE masks.

Installation

1. Install DLWMLS (Required dependency)

git clone https://github.com/CBICA/DLWMLS.git
cd DLWMLS
pip install -e .

2. Install NiChart_DLWMLS

git clone https://github.com/CBICA/NiChart_DLWMLS.git
cd NiChart_DLWMLS
pip install -e .

Usage

Required arguments:

[-fl, --fl_dir] : Name of the input folder with FL scans  (REQUIRED)
[-o, --out_dir] : Name of the output folder for segmentation (REQUIRED)
[--list]        List of MRIDs; first raw (column header) skipped (OPTIONAL)
[--t1_dir]      Name of the input folder with T1 scans  (OPTIONAL)
[--t1_suff]     Suffix of the input T1 scans (OPTIONAL, DEFAULT: _T1.nii.gz)
[--dlmuse_dir]  Name of the input folder with T1 scans  (OPTIONAL)
[--dlmuse_suff] Suffix of the input T1 scans (OPTIONAL, DEFAULT: _T1_LPS_DLMUSE.nii.gz)

Optional arguments:

[-d, --device]  Device to run segmentation ('cuda' (GPU), 'cpu' (CPU) or 
                'mps' (Apple M-series chips supporting 3D CNN))
[-h, --help]    Show this help message and exit.
[-V, --version] Show program's version number and exit.

EXAMPLE USAGE:

NiChart_DLWMLS  --list          /path/to/mrid_list.csv \
                --fl_dir        /path/to/flair_images  \
                --fl_suff       _FL_LPS.nii.gz         \
                --t1_dir        /path/to/t1_images     \
                --t1_suff       _T1_LPS.nii.gz         \
                --dlmuse_dir    /path/to/dlmuse_masks  \
                --dlmuse_suff   _T1_LPS_DLMUSE.nii.gz  \
                --out_dir       /path/to/output