A Python package for evaluating the topological accuracy of a neuron segmentation by computing skeleton-based metrics.
Geometrically-correct, DICOM-native evaluation metrics for medical image segmentation in Python: Dice, IoU, HD95, ASSD, Normalized Surface Dice, per-lesion detection F1, with bootstrap confidence intervals and subgroup/fairness slicing.
Do superpixels survive a lighting change? Benchmarking SLIC superpixel stability under varying illumination, with and without gray-world color constancy — measured by boundary IoU, Variation of Information and neighbor stability.
A medical image kit for segmentation metrics evaluation
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