# Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

arXiv: https://arxiv.org/abs/2004.03675 · HTML: https://mlsanity.com/paper/2004.03675/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation](https://mlsanity.com/paper/2207.04534/) (2022)
- [Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting](https://mlsanity.com/paper/2409.13416/) (2024)
- [Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs](https://mlsanity.com/paper/2103.07240/) (2021)
- [Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging](https://mlsanity.com/paper/2106.00919/) (2021)
- [SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints](https://mlsanity.com/paper/2410.01766/) (2024)
- [Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans](https://mlsanity.com/paper/2110.00948/) (2021)
- [Longitudinal Self-Supervision for COVID-19 Pathology Quantification](https://mlsanity.com/paper/2203.10804/) (2022)
- [SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation](https://mlsanity.com/paper/2412.10946/) (2024)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)
- [Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking](https://mlsanity.com/paper/2605.23118/) (2026)

## Abstract (arXiv)

Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temporal changes. We show the efficacy of our method on a clinical dataset comprised of 70 patients with one follow-up study for each patient. Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation. We improve the result of current state-of-the-art by 2.6% in terms of overall score (p<0.05). Code is publicly available.