Generalizable automated ischaemic stroke lesion segmentation with vision transformers

2025-02-10

AI Summary

This work addresses multiple challenges in automatic ischemic stroke lesion segmentation on diffusion-weighted imaging (DWI), including susceptibility artifacts, morphological heterogeneity of lesions, inter-center scanner variability, time-dependent signal evolution, and scarcity of annotated data. We propose a stroke-domain-optimized vision transformer architecture. Methodologically, we integrate multi-center data (3,563 annotated lesions) and incorporate algorithmic enhancement strategies. For evaluation, we introduce the first comprehensive framework jointly assessing anatomical segmentation accuracy, fairness across clinical subgroups and lesion subtypes, and robustness to imaging equipment variability. Experimental results demonstrate that our method significantly improves generalization and fairness across centers, scanners, and clinical subpopulations—achieving state-of-the-art performance. This advance provides a reliable computational tool for precision stroke diagnosis and pathophysiological investigation.

Technology Category

• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke but poses substantial challenges for automated lesion segmentation: susceptibility artefacts, morphological heterogeneity, age-related comorbidities, time-dependent signal dynamics, instrumental variability, and limited labelled data. Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics that focus on mean performance, neglecting anatomical, subpopulation, and acquisition-dependent variability. Here, we present a high-performance DWI lesion segmentation tool addressing these challenges through optimized vision transformer-based architectures, integration of 3563 annotated lesions from multi-site data, and algorithmic enhancements, achieving state-of-the-art results. We further propose a novel evaluative framework assessing model fidelity, equity (across demographics and lesion subtypes), anatomical precision, and robustness to instrumental variability, promoting clinical and research utility. This work advances stroke imaging by reconciling model expressivity with domain-specific challenges and redefining performance benchmarks to prioritize equity and generalizability, critical for personalized medicine and mechanistic research.

Problem

• Automated segmentation of ischemic stroke lesions
• Overcoming challenges in diffusion-weighted imaging
• Enhancing model equity and generalizability

Innovation

• Vision transformers for lesion segmentation
• Multi-site annotated data integration
• Novel evaluative framework introduced