DLWMLS - Deep Learning White Matter Lesion Segmentation

Overview

DLWMLS uses a trained nnUNet model to segment White Matter Lesions (WML) from ICV-segmented (see DLICV method) and LPS oriented brain images (Nifti/.nii.gz format).

Installation

As a python package

Directly from this repository

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

Installing PyTorch

Depending on your system configuration and supported CUDA version, you may need to follow the PyTorch Installation Instructions.

Usage

A pre-trained nnUNet model can be found at our HuggingFace account. Feel free to use it under the package's license.

From command line (CUDA is recommended)

DLWMLS -i "input_folder" -o "output_folder" -device cuda

For more details, please refer to

DLWMLS -h

[Windows Users] Troubleshooting model download failures

Our model download process creates several deep directory structures. If you are on Windows and your model download process fails, it may be due to Windows file path limitations.

To enable long path support in Windows 10, version 1607, and later, the registry key HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem LongPathsEnabled (Type: REG_DWORD) must exist and be set to 1.

If this affects you, we recommend re-running DLWMLS with the --clear_cache flag set on the first run.

Contact

For more information, please contact CBICA Software.

For Developers

Contributions are welcome! Please refer to our CONTRIBUTING.md for more information on how to report bugs, suggest enhancements, and contribute code. Please make sure to write tests for new code and run them before submitting a pull request.