SegMaST: Mamba-Based Spatio-Temporal Modeling to Improve Longitudinal Detection and Segmentation
MCML Authors
Cosmin Bercea
Dr.
Benedikt Wiestler
Prof. Dr.
Core PI
Daniel Rückert
Prof. Dr.
Director
Abstract
Cosmin Bercea
Dr.
Benedikt Wiestler
Prof. Dr.
Core PI
Daniel Rückert
Prof. Dr.
Director
Abstract
Longitudinal medical image segmentation is fundamental for quantifying disease progression and evaluating treatment efficacy. However, two critical challenges persist: First, methods that jointly segment baseline and follow-up images remain underexplored, often missing the contextual benefits of simultaneous assessment and lacking longitudinal consistency. Second, real-world datasets typically exhibit severe class imbalance between stable and progressive scans — an issue frequently neglected by existing models. To address these limitations, we propose SegMaST, a novel Mamba-based spatio-temporal framework. Unlike conventional approaches that treat timepoints in isolation, SegMaST leverages cross-temporal information and spatial correspondences to jointly segment the initial baseline mask and explicitly localize new pathologies in follow-up scans. Additionally, we introduce an imbalance-aware loss accumulation strategy to enhance robustness in realistic clinical settings. On longitudinal Multiple Sclerosis and Glioma cohorts, SegMaST outperforms established CNN- and attention-based baselines for follow-up segmentation (mean follow-up Dice MS in-house 0.536, MSSEG-2 0.620, and glioma 0.631) and lesion detection (F1 in-house 0.688, MSSEG-2 0.723), while maintaining state-of-the-art accuracy in baseline segmentation (Dice: 0.617 MS, 0.844 glioma).
misc VWL+25
Preprint
Nov. 2025Authors
A. Varma • J. Weidner • L. Lux • C. I. Bercea • M. Mühlau • J. Kirschke • B. Wiestler • D. RückertLinks
URLResearch Area
BibTeXKey: VWL+25