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

TL;DR

SAMba-UNet is a novel dual-encoder architecture combining SAM2, Mamba, and UNet for improved cardiac MRI segmentation, addressing domain shifts and enhancing boundary precision for robotic medical perception.

Contribution

Introduces SAMba-UNet with a Dynamic Feature Fusion Refiner and HOACM, enabling cross-modal feature learning and improved segmentation accuracy in cardiac MRI.

Findings

Achieves Dice of 0.9103 on ACDC benchmark

Significantly improves boundary localization of small structures

Demonstrates robustness for clinical robotic perception applications

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.