Maskel
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.csvwith 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_thinvsskimage.morphology.skeletonizeon 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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