Clinically-aligned ischemic stroke segmentation and ASPECTS scoring on NCCT imaging using a slice-gated loss on foundation representations

• URL: http://arxiv.org/abs/2602.23961v1
• Date: Fri, 27 Feb 2026 12:12:24 GMT
• Title: Clinically-aligned ischemic stroke segmentation and ASPECTS scoring on NCCT imaging using a slice-gated loss on foundation representations
• Authors: Hiba Azeem, Behraj Khan, Tahir Qasim Syed,
• Abstract summary: Most deep learning methods perform pixel-wise segmentation without modeling the structured anatomical reasoning underlying ASPECTS scoring.<n>We propose a clinically aligned framework that combines a frozen DINOv3 backbone with a lightweight decoder.<n>Our method achieves a Dice score of 0.6385 on AISD, outperforming prior CNN and foundation-model baselines.
• Score: 3.186130813218338
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Rapid infarct assessment on non-contrast CT (NCCT) is essential for acute ischemic stroke management. Most deep learning methods perform pixel-wise segmentation without modeling the structured anatomical reasoning underlying ASPECTS scoring, where basal ganglia (BG) and supraganglionic (SG) levels are clinically interpreted in a coupled manner. We propose a clinically aligned framework that combines a frozen DINOv3 backbone with a lightweight decoder and introduce a Territory-Aware Gated Loss (TAGL) to enforce BG-SG consistency during training. This anatomically informed supervision adds no inference-time complexity. Our method achieves a Dice score of 0.6385 on AISD, outperforming prior CNN and foundation-model baselines. On a proprietary ASPECTS dataset, TAGL improves mean Dice from 0.698 to 0.767. These results demonstrate that integrating foundation representations with structured clinical priors improves NCCT stroke segmentation and ASPECTS delineation.

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