Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study

2026-09-08

AI Summary

本文通过比较五种自动化跟踪方法，解决了多发性硬化症脊髓病变纵向追踪的问题，其中基于配准的重叠方法表现最佳。

Abstract

Longitudinal characterization of multiple sclerosis (MS) lesions remains constrained by the lack of frameworks capable of establishing consistent instance-level correspondences across time. Conventional segmentation approaches produce semantic lesion masks at each visit and therefore fail to capture the complex instance temporal patterns associated with lesion appearance, disappearance, splitting, or merging. This study presents a comparative evaluation of five strategies for automated tracking of spinal cord MS lesions in longitudinal MRI data from a multi-site cohort. The investigated strategies rely either on deformable registration or on a spinal anatomical reference system, and encompass overlap-based matching, coordinate-based Hungarian algorithm, gradient-boosted classification, and Siamese model classification. Tracking accuracy is quantified using instance-level true positives, false positives, and false negatives, allowing to assess the presence of one-to-many and many-to-one associations. Results show best performance for the registration-based overlap method. This study provides the first systematic analysis of lesion-instance correspondence in the spinal cord and outlines the strengths and limitations of registration-based and registration-free paradigms for longitudinal MS assessment. The code is available at http://github.com/ivadomed/longitudinal-sc-ms-lesion-tracking .

Problem

• multiple sclerosis
• spinal cord
• longitudinal tracking
• lesion characterization
• instance-level correspondence

Innovation

• deformable registration
• spinal anatomical reference system
• overlap-based matching
• longitudinal MS assessment
• instance-level correspondence