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PreprintCC BY-NC-NDbioRxiv

MaSkel and napari-MaSkel: fast morphological skeleton feature extraction from segmentation masks

  • S. Wittmann
  • D. Pysch
  • S. Uderhardt
  • D. B. Blumenthal
  • A. Moeller1
  1. 1Biomedical Network Science Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universitaet Erlangen-Nuernberg, 91052 Erlange
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Abstract

Existing workflows for extracting morphological features from network-like biomedical structures often require several heterogeneous tools, which can limit integration and reproducibility. We present MaSkel and napari-MaSkel, open-source Python tools that provide GUI- and CLI-based workflows for 2D and 3D skeletonization and graph-based feature extraction, using a substantially accelerated implementation of the widely used Lee94 thinning algorithm. Availability and Implementation: MaSkel and napari-MaSkel are open-source Python packages available under the MIT license on PyPI and GitHub: https://github.com/bionetslab/maskel/, https://github.com/bionetslab/napari-maskel/. Documentation: https://bionetslab.github.io/maskel/, https://bionetslab.github.io/napari-maskel/. Benchmarking and validation: https://github.com/bionetslab/maskel-evaluations.

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Cite

BibTeXRIS
BibTeX
@misc{wittmann2026maskel,
  title = {{MaSkel and napari-MaSkel: fast morphological skeleton feature extraction from segmentation masks}},
  author = {S. Wittmann and D. Pysch and S. Uderhardt and D. B. Blumenthal and A. Moeller},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.15.751651},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.15.751651v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - MaSkel and napari-MaSkel: fast morphological skeleton feature extraction from segmentation masks
AU  - S. Wittmann
AU  - D. Pysch
AU  - S. Uderhardt
AU  - D. B. Blumenthal
AU  - A. Moeller
PY  - 2026
DA  - 2026/09/15
PB  - bioRxiv
DO  - 10.64898/2026.09.15.751651
UR  - https://www.biorxiv.org/content/10.64898/2026.09.15.751651v1
AB  - Existing workflows for extracting morphological features from network-like biomedical structures often require several heterogeneous tools, which can limit integration and reproducibility. We present MaSkel and napari-MaSkel, open-source Python tools that provide GUI- and CLI-based workflows for 2D and 3D skeletonization and graph-based feature extraction, using a substantially accelerated implementation of the widely used Lee94 thinning algorithm. Availability and Implementation: MaSkel and napari-MaSkel are open-source Python packages available under the MIT license on PyPI and GitHub: https://github.com/bionetslab/maskel/, https://github.com/bionetslab/napari-maskel/. Documentation: https://bionetslab.github.io/maskel/, https://bionetslab.github.io/napari-maskel/. Benchmarking and validation: https://github.com/bionetslab/maskel-evaluations.
ER  -