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

Pierre-Louis Benveniste, Shannon Kolind, Larry Lynd, Sarah A. Morrow, Jiwon Oh, Alexandre Prat, Alice Schabas, Penny Smyth, Roger Tam, Anthony Traboulsee, Herve Lombaert, Julien Cohen-Adad

Keywords: Multiple Sclerosis, Longitudinal, MRI, Tracking, Lesion, Segmentation

TL;DR: Evaluation of strategies to track multiple sclerosis lesions across multiple time points.

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: https://github.com/ivadomed/ms-lesion-agnostic

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Neuroimaging

Registration Requirement: Yes

Reproducibility: https://github.com/ivadomed/ms-lesion-agnostic

Visa & Travel: Yes

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 17