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

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Radiology

Background:

• Accurate delineation of intracerebral hemorrhage (ICH) on non-contrast CT (NCCT) is crucial for patient management.
• Current voxel-wise segmentation methods struggle with low-contrast boundaries and calcifications, leading to inaccurate volume estimation and risk stratification.

Purpose of the Study:

• To introduce HemaContour, a contour-centric framework for precise hematoma boundary segmentation on NCCT.
• To evaluate HemaContour's performance against state-of-the-art methods in terms of segmentation accuracy, boundary fidelity, and volumetric agreement.

Main Methods:

• HemaContour utilizes a closed parametric spline fitted to the hematoma boundary, seeded by a CNN and optimized via an implicit contour-regression network.
• The framework incorporates a shape-aware objective function and differentiable snake dynamics for refinement, ensuring smooth and anatomically plausible contours.
• Performance was evaluated on the INSTANCE dataset and externally validated on the PhysioNet CT-ICH dataset.

Main Results:

• HemaContour achieved superior Dice scores (87.2% on INSTANCE, 84.3% on PhysioNet CT-ICH) compared to the best baseline (Swin-UNETR).
• It significantly reduced Hausdorff distance 95th percentile (HD95) by ~14.1% on both datasets, indicating improved boundary accuracy.
• The method demonstrated better volumetric agreement and a smaller generalization gap, with improved performance near edema and calcifications.

Conclusions:

• HemaContour offers a robust contour-centric alternative to voxel-wise segmentation for ICH on NCCT.
• The framework enhances boundary delineation and volumetric accuracy, providing interpretable shape metrics for clinical application.
• Its practical runtime and improved performance highlight its potential for clinical translation in ICH analysis.