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https://github.com/cbica/dlmuse

A repository that allows users to apply the DLMUSE method to their brain imaging data.

https://github.com/cbica/dlmuse

Science Score: 26.0%

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    CITATION.cff file
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    Scientific vocabulary similarity
    Low similarity (17.5%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

A repository that allows users to apply the DLMUSE method to their brain imaging data.

Basic Info
  • Host: GitHub
  • Owner: CBICA
  • License: other
  • Language: Python
  • Default Branch: main
  • Size: 16.1 MB
Statistics
  • Stars: 11
  • Watchers: 2
  • Forks: 0
  • Open Issues: 2
  • Releases: 2
Created over 2 years ago · Last pushed 10 months ago
Metadata Files
Readme Contributing License

README.md

macos tests ubuntu tests PyPI Stable

DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters

Overview

DLMUSE uses a trained nnUNet model to compute the segmentation of the brain into MUSE ROIs from the nifti image of the Intra Cranial Volume (ICV - see DLICV method), oriented in LPS orientation. It produces the segmented brain, along with a .csv file of the calculated volumes of each ROI.

Installation

As a python package

bash pip install DLMUSE

Directly from this repository

bash git clone https://github.com/CBICA/DLMUSE cd DLMUSE 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 hugging face account. Feel free to use it under the package's license.

From command line

bash DLMUSE -i "input_folder" -o "output_folder" -device cpu

In-code usage

python from DLMUSE import run_dlmuse_pipeline ... run_dlmuse_pipeline(in_dir, out_dir, device)

For more details, please refer to

bash DLMUSE -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 DLMUSE 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
  • Watch event: 8
  • Issue comment event: 3
  • Push event: 269
  • Pull request review event: 3
  • Pull request review comment event: 2
  • Pull request event: 6
  • Create event: 3
Last Year
  • Issues event: 1
  • Watch event: 8
  • Issue comment event: 3
  • Push event: 269
  • Pull request review event: 3
  • Pull request review comment event: 2
  • Pull request event: 6
  • Create event: 3

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 1
  • Total pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: about 1 month
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.67
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 3
  • Average time to close issues: N/A
  • Average time to close pull requests: about 1 month
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.67
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • euroso97 (2)
  • spirosmaggioros (1)
Pull Request Authors
  • spirosmaggioros (3)
  • euroso97 (2)
  • AlexanderGetka-cbica (1)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels
bug (2)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 53 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 6
  • Total maintainers: 2
pypi.org: dlmuse

DLMUSE - Deep Learning MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 53 Last month
Rankings
Dependent packages count: 9.9%
Average: 38.8%
Dependent repos count: 67.8%
Maintainers (2)
AlexanderGetka gaidinis
Last synced: 10 months ago