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

2026-02-27

AI Summary

This study addresses a critical limitation in current deep learning approaches for segmenting ischemic stroke lesions on non-contrast CT (NCCT) scans: the neglect of the anatomical coupling between the basal ganglia (BG) and supraganglionic (SG) levels as defined by the ASPECTS scoring system, which undermines alignment with clinical evaluation logic. To bridge this gap, the authors propose the first integration of this structured clinical prior into foundation model training through an anatomy-aware gated loss (TAGL). By combining a frozen DINOv2 backbone with a lightweight decoder, their method enforces BG-SG consistency without increasing inference overhead. Evaluated on the AISD dataset, the approach achieves a Dice score of 0.6385, outperforming existing CNNs and foundation models. On an in-house ASPECTS dataset, it further improves the average Dice from 0.698 to 0.767, significantly enhancing segmentation consistency with clinical standards.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• ischemic stroke
• ASPECTS scoring
• NCCT imaging
• anatomical reasoning
• stroke segmentation

Innovation

• foundation model
• Territory-Aware Gated Loss
• ASPECTS scoring
• ischemic stroke segmentation
• clinical alignment