HemaContour: Parametric Contour-Based ICH Segmentation in NCCT

Overview

HemaContour introduces a novel contour-centric framework for intracerebral hemorrhage (ICH) segmentation on non-contrast CT, improving boundary accuracy and volumetric estimation over state-of-the-art voxel-wise methods. It achieves higher Dice scores and reduced boundary errors on both internal and external datasets, demonstrating enhanced robustness and clinical interpretability.

Background

Intracerebral hemorrhage (ICH) is a critical neurological emergency with high mortality and morbidity, where hematoma volume estimation from non-contrast CT (NCCT) is essential for prognosis and treatment decisions. Manual segmentation is the clinical gold standard but is time-consuming and variable, while traditional volumetric approximations often fail for irregular hematomas. Deep learning voxel-wise segmentation methods have advanced automated delineation but struggle with low contrast boundaries, edema interference, and produce jagged contours that limit clinical trust. Contour-based segmentation offers an anatomically plausible alternative by explicitly modeling smooth boundaries, yet prior methods lacked robustness and integration with deep learning for ICH.

Data Highlights

Key Findings

• HemaContour achieves superior Dice scores (87.2% vs. 85.0%) and reduces Hausdorff distance (HD95) by ~14.1% on the INSTANCE dataset compared to the best voxel-wise baseline.
• On external validation with PhysioNet CT–ICH, HemaContour maintains improved performance with Dice 84.3% vs. 81.8% and HD95 8.5 mm vs. 9.9 mm, demonstrating better generalizability.
• Volumetric agreement metrics show HemaContour reduces absolute volume error (AVE) and relative volume error (RVE), indicating more accurate hematoma volume estimation.
• The contour-based approach yields smoother, anatomically plausible boundaries with fewer extreme boundary excursions near edema and calcifications, enhancing clinical interpretability.
• HemaContour exhibits greater robustness to mild imaging artifacts and smaller generalization gaps compared to voxel/transformer-based models.
• Runtime efficiency is practical for clinical use, processing approximately 12 milliseconds per CT slice.

Clinical Implications

HemaContour’s contour-centric segmentation provides clinicians with more accurate and reliable hematoma boundaries and volume measurements, critical for risk stratification and treatment planning in ICH. Its improved boundary fidelity and shape metrics enable enhanced prognostic assessment beyond volume alone. The method’s robustness and efficiency support integration into clinical workflows for timely decision-making.

Conclusion

By explicitly modeling hematoma boundaries as parametric contours refined through deep learning and snake dynamics, HemaContour advances ICH segmentation accuracy and interpretability on NCCT. This approach offers a robust, clinically relevant alternative to voxel-wise methods, facilitating improved volumetric and shape-based prognostication.

References

1. HemaContour Study 2024 -- HemaContour: A Parametric Approach for Enhanced ICH Segmentation in Non-Contrast CT Imaging