OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation
Journal:
arXiv
Published Date:
Apr 13, 2025
Abstract
Accurate segmentation of longitudinal CT scans is important for monitoring
tumor progression and evaluating treatment responses. However, existing 3D
segmentation models solely focus on spatial information. To address this gap,
we propose OmniMamba4D, a novel segmentation model designed for 4D medical
images (3D images over time). OmniMamba4D utilizes a spatio-temporal
tetra-orientated Mamba block to effectively capture both spatial and temporal
features. Unlike traditional 3D models, which analyze single-time points,
OmniMamba4D processes 4D CT data, providing comprehensive spatio-temporal
information on lesion progression. Evaluated on an internal dataset comprising
of 3,252 CT scans, OmniMamba4D achieves a competitive Dice score of 0.682,
comparable to state-of-the-arts (SOTA) models, while maintaining computational
efficiency and better detecting disappeared lesions. This work demonstrates a
new framework to leverage spatio-temporal information for longitudinal CT
lesion segmentation.
