SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception

Guohao Huo; Ruiting Dai; Ling Shao; Hao Tang

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arXiv · 2505.16304

SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception

Guohao Huo·Ruiting Dai·Ling Shao·Hao Tang

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Abstract

To address complex pathological feature extraction in automated cardiac MRI segmentation, we propose SAMba-UNet, a novel dual-encoder architecture that synergistically combines the vision foundation model SAM2, the linear-complexity state-space model Mamba, and the classical UNet to achieve cross-modal collaborative feature learning; to overcome domain shifts between natural images and medical scans, we introduce a Dynamic Feature Fusion Refiner that employs multi-scale pooling and channel-spatial dual-path calibration to strengthen small-lesion and fine-structure representation, and we design a Heterogeneous Omni-Attention Convergence Module (HOACM) that fuses SAM2's local positional semantics with Mamba's long-range dependency modeling via global contextual attention and branch-selective emphasis, yielding substantial gains in both global consistency and boundary precision-on the ACDC cardiac MRI benchmark, SAMba-UNet attains a Dice of 0.9103 and HD95 of 1.0859 mm, notably improving boundary localization for challenging structures like the right ventricle, and its robust, high-fidelity segmentation maps are directly applicable as a perception module within intelligent medical and surgical robotic systems to support preoperative planning, intraoperative navigation, and postoperative complication screening; the code will be open-sourced to facilitate clinical translation and further validation.

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Guohao Huo, Ruiting Dai, Ling Shao, Hao Tang. 2025-09-09. SAMba-UNet: SAM2-Mamba UNet for Cardiac MRI in Medical Robotic Perception. https://arxiv.org/abs/2505.16304

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