发表机构
Polytechnique Montreal; TUM University Hospital; University of British Columbia; University of Calgary; University of Toronto; Université de Montréal; University of Alberta（蒙特利尔理工学院; 慕尼黑工业大学附属医院; 不列颠哥伦比亚大学; 卡尔加里大学; 多伦多大学; 蒙特利尔大学; 阿尔伯塔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究比较五种策略追踪脊髓多发性硬化病灶，基于配准的重叠方法性能最佳，首次系统分析病灶实例对应关系。
AI 中文摘要
多发性硬化（MS）病灶的纵向表征仍受限于缺乏能够在时间上建立一致的实例级对应关系的框架。传统的分割方法在每次就诊时产生语义病灶掩膜，因此无法捕捉与病灶出现、消失、分裂或合并相关的复杂实例时间模式。本研究对来自多中心队列的纵向MRI数据中五种脊髓MS病灶自动追踪策略进行了比较评估。所研究的策略依赖于可变形配准或脊髓解剖参考系统，并包括基于重叠的匹配、基于坐标的匈牙利算法、梯度提升分类和孪生模型分类。追踪准确性通过实例级真阳性、假阳性和假阴性进行量化，从而评估一对多和多对一关联的存在。结果显示，基于配准的重叠方法性能最佳。本研究首次对脊髓中病灶实例对应关系进行了系统分析，并概述了基于配准和无配准范式在纵向MS评估中的优势与局限性。代码可在以下网址获取：此http URL。
英文摘要
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 .
Comments10 pages, 5 figures
