# Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

arXiv: https://arxiv.org/abs/2409.13416 · HTML: https://mlsanity.com/paper/2409.13416/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [nnDetection: A Self-configuring Method for Medical Object Detection](https://mlsanity.com/paper/2106.00817/) (2021)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2004.03675/) (2020)
- [An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation](https://mlsanity.com/paper/2207.04534/) (2022)
- [CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation](https://mlsanity.com/paper/2307.04513/) (2023)

## Built on by (most cited first, 8 of 8)

- [3D-RAD: A Comprehensive 3D Radiology Med-VQA Dataset with Multi-Temporal Analysis and Diverse Diagnostic Tasks](https://mlsanity.com/paper/2506.11147/) (2025)
- [LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI](https://mlsanity.com/paper/2508.00496/) (2025)
- [MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI](https://mlsanity.com/paper/2510.27326/) (2025)
- [Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis](https://mlsanity.com/paper/2507.19230/) (2025)
- [LinGuinE: Longitudinal Guidance Estimation for Volumetric Tumour Segmentation](https://mlsanity.com/paper/2506.06092/) (2025)
- [Promptable Longitudinal Lesion Segmentation in Whole-Body CT](https://mlsanity.com/paper/2509.00613/) (2025)
- [Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models](https://mlsanity.com/paper/2605.09666/) (2026)
- [Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study](https://mlsanity.com/paper/2609.09424/) (2026)

## Abstract (arXiv)

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at www.github.com/MIC-DKFZ/Longitudinal-Difference-Weighting.