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Maskel

PyPI version Python version License

Vessel Skeletonization and Graph-Based Phenotype Analysis in Retinal Fundus Images and other tubular structures.

Maskel is the core algorithm package: thinning, feature extraction, and the batch CLI. For the napari plugin, see napari-maskel. For benchmarks and the HRF-based analysis notebooks, see maskel-evaluations.

Installation

uv sync                  # core only
uv sync --extra dev      # + test tools

CLI

maskel init config.json
maskel validate config.json
maskel run --input /path/to/images --config config.json --out outputs

A config JSON can also be exported from the napari-maskel plugin's Save Config button and used here directly — both consume the same schema (see below).

CLI outputs:

  • outputs/summary.csv with one feature row per image
  • Optional per-image skeleton outputs (default: .npy)
  • Optional per-image branch tables when output.write_branch_csv=true
  • Optional per-image node tables when output.write_node_csv=true
  • Optional per-image skeleton graphs when output.write_graphml=true

Configuration

Extraction and output settings are defined in a JSON config file (e.g. the one exported from napari or written by hand).

{
  "schema_version": 3,
  "extraction": {
    "branches": false,
    "branch_color_property": "tortuosity",
    "branch_text": false,
    "nodes": false,
    "summary": true,
    "fractal_dimension": false,
    "vessel_radius": false,
    "junction_cleanup": false,
    "cleanup_threshold_factor": 2.5,
    "prune_spurs": false,
    "min_spur_length": 10.0,
    "spur_iterations": 1,
    "closing_iterations": 0,
    "fill_holes": false,
    "max_hole_size": 0,
    "show_preprocessed": false
  },
  "output": {
    "write_skeleton_npy": true,
    "write_skeleton_png": false,
    "write_summary_csv": true,
    "write_branch_csv": false,
    "write_node_csv": false,
    "write_radius": false,
    "write_graphml": false
  }
}
Key Type Default Description
extraction.branches bool false Extract per-branch features for CSV export or napari visualization
extraction.branch_color_property str "tortuosity" Branch property used to color the napari shapes layer; one of tortuosity, straightness, mean_radius, std_radius, volume, surface_area, ...
extraction.branch_text bool false Display branch ID, length, and tortuosity labels on the napari branch layer
extraction.nodes bool false Extract per-node features for CSV export or napari visualization
extraction.summary bool true Compute summary features
extraction.fractal_dimension bool false Compute fractal dimension of the skeleton
extraction.vessel_radius bool false Estimate vessel radius using EDT from the segmentation
extraction.junction_cleanup bool false Clean up ambiguous junction pixels after thinning
extraction.cleanup_threshold_factor float 2.5 Sensitivity for junction cleanup (higher = larger cycles get collapsed)
extraction.prune_spurs bool false Remove short endpoint-to-junction branches (thinning spur artifacts) after skeletonization
extraction.min_spur_length float 10.0 Branches shorter than this (in pixels) qualify as spurs when prune_spurs is true
extraction.spur_iterations int 1 How often pruning is repeated on its own output, since removing a spur can expose new ones
extraction.closing_iterations int 0 Morphological closing iterations applied before thinning (0 = disabled)
extraction.fill_holes bool false Fill holes in the binary segmentation before thinning
extraction.max_hole_size int 0 Maximum hole area (px) to fill when fill_holes is true; 0 = fill all
extraction.show_preprocessed bool false Show preprocessed binary layer (after closing and hole filling) in the napari viewer
output.write_skeleton_npy bool true Save skeleton as .npy (NumPy array) per image
output.write_skeleton_png bool false Save binary skeleton mask as .png per image
output.write_summary_csv bool true Write aggregated per-image features to summary.csv
output.write_branch_csv bool false Write per-branch CSV tables (requires extraction.branches)
output.write_node_csv bool false Write per-node CSV tables (requires extraction.nodes)
output.write_radius bool false Write per-pixel radius matrix as .npy (requires extraction.vessel_radius)
output.write_graphml bool false Write skeleton graph as .graphml per image (nodes = graph nodes, edges = branches)

Shell completions

# zsh
eval "$(maskel completions zsh)"

# bash
eval "$(maskel completions bash)"

# PowerShell
maskel completions powershell | Out-String | Invoke-Expression

Add the appropriate line to your shell rc for persistent tab-completion.

Tests

uv sync --extra dev && pytest                     # all tests
uv sync --extra dev && pytest -m "not slow"       # skip regression tests
  • 3D regression - thinning + features on a brain volume (from scikit-image), compared against saved baselines
  • 3D comparison - maskel lee94_thin vs skimage.morphology.skeletonize on the brain volume, asserting identical output

First run (or --update-baseline) generates baselines in tests/skeletons/ and tests/features/.

Real-data regression tests against the HRF dataset (2D thinning + feature extraction on all 45 samples) live in maskel-evaluations, since they depend on that external dataset.

License

Maskel is released under the MIT License. See LICENSE for details.

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