OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation

• URL: http://arxiv.org/abs/2504.09655v2
• Date: Thu, 24 Apr 2025 04:25:34 GMT
• Title: OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation
• Authors: Justin Namuk Kim, Yiqiao Liu, Rajath Soans, Keith Persson, Sarah Halek, Michal Tomaszewski, Jianda Yuan, Gregory Goldmacher, Antong Chen,
• Abstract summary: We propose OmniMamba4D, a 4D segmentation model designed for 3D medical images.<n>Unlike traditional 3D models, which analyze single-time points, OmniMamba4D processes 4D scans, providing comprehensive-temporal information on lesion progression.<n> 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.
• Score: 0.7490096698922335
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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