skan
Skeleton analysis in Python
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no security vulnerabilities, installs cleanly, and fills a specific niche in skeleton image analysis with a mature dependency stack. It is appropriate for research and production use in image analysis pipelines. The Alpha status reflects ongoing development but not instability.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; input images should be binary (foreground/background) skeletons.
- Low friction: pure Python wheel with no compiled dependencies.
- Active maintenance with recent commits and a stable dependency stack of well-established scientific libraries.
License · maintenance · safety
BSD 3-Clause (permissive) — BSD 3-Clause is permissive; you can use, modify, and distribute this package freely in commercial and open-source projects with minimal restrictions.
last release 2026-02-06 (189 days) · last repo commit 2026-06-26 · 147 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 130,711 downloads/mo, #11,629 on PyPI
Alternatives
Verify before relying
import skan
import imageio
# Load a binary skeleton image
image = imageio.imread('skeleton.png')
# Analyze the skeleton
results = skan.csr.summarise(skan.csr.skeleton_to_csgraph(image))- Whether the package handles 3D skeleton images or is limited to 2D
- Performance characteristics on very large images or complex skeleton networks
- Specific output metrics and how they relate to standard morphological descriptors
What it is and what it does
skan is a Python library for analyzing skeleton images—binary images where objects have been reduced to their thin, one-pixel-wide centerlines. It extracts topological and morphological information from these skeletons, such as branch points, endpoints, and path lengths, representing them as graph structures. The package is built on established scientific libraries (NumPy, SciPy, scikit-image, NetworkX) and integrates with napari for visualization.
Typical use is in biomedical imaging (analyzing blood vessel networks, neuron morphology), materials science (fiber networks, crack patterns), and any domain where understanding the connectivity and geometry of thin structures matters. You load a binary skeleton image, convert it to a graph representation, and query its structural properties.
Use it for
- Quantify branching patterns and connectivity in microscopy images of blood vessels or neural networks.
- Extract path lengths and tortuosity metrics from fiber or crack networks in materials.
- Identify and classify junction types and endpoints in road or river network maps.
- Measure skeleton robustness by analyzing how removal of nodes affects network connectivity.
- Generate summary statistics on skeleton morphology for automated image classification tasks.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no security vulnerabilities, installs cleanly, and fills a specific niche in skeleton image analysis with a mature dependency stack. It is appropriate for research and production use in image analysis pipelines. The Alpha status reflects ongoing development but not instability.
Install
skan on PyPI
Before you install
Low friction: pure Python wheel with no compiled dependencies. Active maintenance with recent commits and a stable dependency stack of well-established scientific libraries.
Requires Python 3.9 or later; input images should be binary (foreground/background) skeletons.
License in practice
BSD 3-Clause is permissive; you can use, modify, and distribute this package freely in commercial and open-source projects with minimal restrictions.
Quickstart
import skan
import imageio
# Load a binary skeleton image
image = imageio.imread('skeleton.png')
# Analyze the skeleton
results = skan.csr.summarise(skan.csr.skeleton_to_csgraph(image))Verify before relying
- Whether the package handles 3D skeleton images or is limited to 2D
- Performance characteristics on very large images or complex skeleton networks
- Specific output metrics and how they relate to standard morphological descriptors
Package facts
| License | BSD 3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 11 packagesimageiomatplotlibnetworkxnumbanumpypandasopenpyxlscikit-imagescipytoolztqdm |
| Maintenance | Actively maintained 189 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 130,711 / month, #11,629 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaEnvironment :: ConsoleFramework :: napariIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: skan-0.13.1-py3-none-any.whl
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See also scikit-image · shapely-polyskel · momepy · pymupdf-layout · connected-components-3d · anastruct · imutils · napari · pycat-napari · imagesize