ArticleProceedings. IEEE International Symposium on Biomedical Imaging2026
NEUSEG: INTERPRETABLE UNSUPERVISED GM/WM SEGMENTATION IN BRAIN HISTOPATHOLOGY USING NUCLEI MORPHOMETRICS.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We present NEUSEG, a fully automated, interpretable, and unsupervised pipeline for gray/white matter (GM/WM) segmentation in brain histopathology whole-slide images (WSIs). GM/WM segmentation in WSIs is challenging due to their gigapixel-scale resolution and substantial variability in staining, regional morphology, and underlying pathology. To address this, NEUSEG combines nuclei morphometrics with a two-component Gaussian Mixture Model and refines boundaries using a Conditional Random Field and morphological post-processing. In comparison with a supervised CNN baseline, NEUSEG achieved comparable accuracy on in-distribution slides and superior robustness under out-of-distribution conditions. Across 252 WSIs with expert-annotated regions of interest (ROIs), median annotation-to-contour distances were 32.89 μm for the GM-BG boundary and 129.79 μm for the GM-WM boundary, with no significant difference across stains or pathology types and only minor regional variation. Percent area occupied between expert- and segmentation-derived ROIs showed near-perfect agreement (
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