Work

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

Year: 2026

Type: preprint

Abstract: Summary: 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 .

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.09.15.751651

Authors: Simon Wittmann, Dominik Pysch, Stefan Uderhardt, David B. Blumenthal, Anna Möller

Institutions: Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsklinikum Erlangen

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Friedrich-Alexander-Universität Erlangen-Nürnberg, Deutsche Forschungsgemeinschaft

Awards: G1865481633, G3912926014