DEFUSE-MS: Deformation Field-Guided Spatiotemporal Graph-Based Framework for Multiple Sclerosis New Lesion Detection
Salem, Mostafa Hassan, Salma Papineni, Vijay Ram Kumar Elsayed, Ayman Yaqub, Mohammad
Salem, Mostafa
Hassan, Salma
Papineni, Vijay Ram Kumar
Elsayed, Ayman
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Longitudinal magnetic resonance imaging (MRI) is essential for diagnosing and monitoring multiple sclerosis (MS), a chronic central nervous system disorder. Tracking brain lesion evolution over time is essential for predicting MS progression, yet this process is time-consuming and subject to intra- and interobserver variability. While deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) have been applied to lesion detection, they often struggle to fully capture spatial, structural and temporal relationships. Vision graph neural networks (ViGs) present a novel approach with the potential to improve performance in these tasks by effectively capturing relational and structural information. We introduce DEFUSE-MS, a Deformation Field-Guided Spatiotemporal ViG-Based Framework for detecting MS new T2-weighted lesions. The framework features a Heterogeneous Spatiotemporal Graph Module (HSTGM), which functions as both an encoder and decoder. Evaluated on the MSSEG-II dataset, DEFUSE-MS achieves state-of-the-art performance with a lesion detection F1 score of 0.65, sensitivity (SensL) of 0.74, positive predictive value (PPVL) of 0.65, and a mean segmentation Dice score of 0.55, outperforming the state-of-the-art methods. These results highlight DEFUSE-MS’s efficacy in MS new lesion detection. The code is available at https://github.com/BioMedIA-MBZUAI/DEFUSE-MS.
Citation
M. Salem, S. Hassan, V. Ram, K. Papineni, A. Elsayed, and M. Yaqub, “DEFUSE-MS: Deformation Field-Guided Spatiotemporal Graph-Based Framework for Multiple Sclerosis New Lesion Detection,” pp. 257–267, 2026, doi: 10.1007/978-3-032-05162-2_25
Source
Medical Image Computing and Computer Assisted Intervention
Conference
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Keywords
Brain, MRI, Multiple Sclerosis, Deep Learning, Spatiotemporal Learning, Vision Graph Neural Network
Subjects
Source
28 MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention
Publisher
Springer Nature