Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models

Researchers argue that Multiple Sclerosis lesion segmentation models are inadequately evaluated using only Dice scores, ignoring lesion-wise detection performance and metrics relevant to clinical practice. The paper proposes rethinking evaluation frameworks to better assess deep learning models for real-world hospital deployment in MS diagnosis and progression monitoring.

Current deep learning approaches for MS lesion detection rely heavily on the Dice coefficient as a primary evaluation metric, a practice that obscures clinically significant performance gaps. This work identifies a critical disconnect between how models are benchmarked in academic settings and what medical professionals actually need to assess disease progression. Neurologists require different information than what aggregate pixel-level metrics provide: they need to understand lesion-by-lesion detection rates, false positive distributions, and performance in ambiguous cases that challenge human annotators.

The medical imaging field has increasingly recognized that traditional computer vision metrics often fail to capture clinical utility. MS lesion segmentation exemplifies this problem because missed or misidentified lesions directly impact treatment decisions and disease monitoring. The authors' emphasis on problem fingerprinting—understanding what neurologists prioritize in MRI analysis—provides a framework for developing evaluation metrics aligned with clinical workflows rather than academic conventions.

For healthcare AI adoption, this research addresses a persistent barrier to deployment. Hospitals cannot confidently integrate models with unclear real-world performance characteristics. By demonstrating that state-of-the-art models may underperform on clinically critical cases, the paper highlights why institutional adoption remains slow despite impressive benchmark scores. The development of clinically-grounded evaluation metrics reduces deployment risk and accelerates trustworthy AI integration into medical practice. This work establishes evaluation standards that could become prerequisites for regulatory approval and hospital procurement decisions.

• →Dice score alone inadequately captures MS lesion segmentation model performance for clinical contexts
• →Lesion-wise detection metrics and performance on confusing cases better reflect real-world clinical utility
• →Current state-of-the-art models may have significant blind spots undetected by conventional metrics
• →Problem fingerprinting aligned with neurologist workflows improves evaluation framework design
• →Clinically-grounded metrics are essential for trustworthy hospital deployment of AI models