NEUSEG: Interpretable Unsupervised GM/WM Segmentation in Brain Histopathology Using Nuclei Morphometrics

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 GMBG 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 expertand segmentation-derived ROIs showed near-perfect agreement (r=0.987). NEUSEG is lightweight, CPU-operable, and processes each WSI within minutes, enabling scalable and robust GM/WM segmentation across heterogeneous histopathology datasets.