HemaContour: explicit parametric contour learning for robust ICH segmentation on non-contrast CT

Accurate hematoma delineation on non-contrast CT (NCCT) is pivotal for intracerebral hemorrhage (ICH) volume estimation and risk stratification, yet voxel-wise segmenters often fray at low-contrast edges or near calcifications, inflating boundary errors and biasing volumetry. We present HemaContour, a contour-centric framework that fits a closed parametric spline to the hematoma boundary. A coarse CNN seeds the contour, which is then optimized by an implicit contour-regression network trained with a shape-aware objective (overlap, boundary, curvature). Refinement is performed via differentiable snake dynamics, yielding smooth, anatomically plausible contours and native access to volume and shape metrics. On INSTANCE, HemaContour attains Dice 87.2% versus 85.0% for the best baseline (Swin-UNETR) and reduces HD95 from 8.5 mm to 7.3 mm (~14.1%). On PhysioNet CT-ICH (external validation), it maintains Dice 84.3% vs. 81.8% and HD95 8.5 mm vs. 9.9 mm (again ~14.1% improvement), with better volumetric agreement (AVE 4.3 mL vs. 5.0 mL; RVE 11.1% vs. 12.7%). The generalization gap is smaller for HemaContour (Dice Δ = 2.9 pp) than strong voxel/transformer baselines (3.2-3.6 pp). Qualitative stress tests highlight fewer extreme boundary excursions at edema interfaces, improved specificity near calcifications, and stability under mild artifacts. Runtime is practical (~12 ms/slice). By re-centering learning on an explicit, image-aware contour, HemaContour improves boundary fidelity and volumetric accuracy while preserving interpretability and readiness for clinical shape analytics, offering a robust alternative to purely voxel-centric segmentation for ICH NCCT.