Longitudinal deep learning-based segmentation of multiple sclerosis lesions in a local clinical cohort

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Purpose Automated monitoring of multiple sclerosis (MS) lesion progression remains challenging in clinical practice. This study evaluates LongiSeg, a longitudinal deep learning architecture, on heterogeneous clinical data to assess its practical value for routine MS radiological monitoring and extends its use for new lesion detection. Methods We trained LongiSeg on 470 patients from a diverse clinical cohort acquired across 15 scanner types (both 1.5T and 3T) with mixed 2D/3D T2w FLAIR sequences. We trained and evaluated models for both cross-sectional lesion segmentation and new lesion detection. Performance was compared against a single-timepoint nnU-Net on a cross-sectional test cohort consisting of 67 patients as well as 22 patients presenting with new lesions. Results LongiSeg achieved superior cross-sectional segmentation performance compared to single-timepoint nnU-Net on the clinical test set ( n = 67, T2w FLAIR input, DSC: 0.684 ± 0.124 vs. DSC: 0.665 ± 0.144). LongiSeg for new lesion detection obtained a low performance on the small clinical test cohort ( n = 22, DSC: 0.260, 95%CI: 0.143–0.383). Conclusion LongiSeg demonstrated moderate improvements in cross-sectional MS lesion segmentation. However, the added complexity of processing longitudinal scans may not be justified by these modest gains. For new lesion segmentation, performance was low, especially in cases with few lesions.

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