https://github.com/cbica/dlwmls

https://github.com/cbica/dlwmls

Science Score: 26.0%

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Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: CBICA
  • License: other
  • Language: Python
  • Default Branch: main
  • Size: 1000 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Created over 1 year ago · Last pushed 10 months ago
Metadata Files
Readme Contributing License

README.md

DLWMLS - Deep Learning White Matter Lesion Segmentation

MacOS Build Ubuntu Build

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

bash 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)

bash DLWMLS -i "input_folder" -o "output_folder" -device cuda The files in the input folder must have "FLLPS.nii.gz" as prefix.

In-code usage

python from DLWMLS import run_dlwmls_pipeline ... run_dlwmls_pipeline(in_dir, out_dir, device) For more details, please refer to

bash 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.

Owner

  • Name: Center for Biomedical Image Computing & Analytics (CBICA)
  • Login: CBICA
  • Kind: organization
  • Email: software@cbica.upenn.edu
  • Location: Philadelphia, PA

CBICA focuses on the development and application of advanced computation techniques.

GitHub Events

Total
  • Issues event: 1
  • Delete event: 1
  • Push event: 19
  • Public event: 1
  • Pull request event: 12
  • Create event: 2
Last Year
  • Issues event: 1
  • Delete event: 1
  • Push event: 19
  • Public event: 1
  • Pull request event: 12
  • Create event: 2

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 2
  • Total pull requests: 6
  • Average time to close issues: 11 days
  • Average time to close pull requests: 8 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 2
Past Year
  • Issues: 2
  • Pull requests: 6
  • Average time to close issues: 11 days
  • Average time to close pull requests: 8 days
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 2
Top Authors
Issue Authors
  • spirosmaggioros (1)
  • euroso97 (1)
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  • spirosmaggioros (2)
  • dependabot[bot] (2)
  • euroso97 (2)
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Dependencies

requirements.txt pypi
  • argparse *
  • huggingface_hub *
  • nnunetv2 >=2.2.1
  • pathlib *
  • torch >=2.2.1
setup.py pypi