ABS-Mamba: SAM2-Driven Bidirectional Spiral Mamba Network for Medical Image Translation

• URL: http://arxiv.org/abs/2505.07687v1
• Date: Mon, 12 May 2025 15:51:15 GMT
• Title: ABS-Mamba: SAM2-Driven Bidirectional Spiral Mamba Network for Medical Image Translation
• Authors: Feng Yuan, Yifan Gao, Wenbin Wu, Keqing Wu, Xiaotong Guo, Jie Jiang, Xin Gao,
• Abstract summary: ABS-Mamba is a novel architecture for organ-aware semantic representation.<n>CNNs preserve modality-specific edge and texture details.<n>Mamba's selective state-space modeling for efficient long- and short-range feature dependencies.
• Score: 20.242887183708653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate multi-modal medical image translation requires ha-rmonizing global anatomical semantics and local structural fidelity, a challenge complicated by intermodality information loss and structural distortion. We propose ABS-Mamba, a novel architecture integrating the Segment Anything Model 2 (SAM2) for organ-aware semantic representation, specialized convolutional neural networks (CNNs) for preserving modality-specific edge and texture details, and Mamba's selective state-space modeling for efficient long- and short-range feature dependencies. Structurally, our dual-resolution framework leverages SAM2's image encoder to capture organ-scale semantics from high-resolution inputs, while a parallel CNNs branch extracts fine-grained local features. The Robust Feature Fusion Network (RFFN) integrates these epresentations, and the Bidirectional Mamba Residual Network (BMRN) models spatial dependencies using spiral scanning and bidirectional state-space dynamics. A three-stage skip fusion decoder enhances edge and texture fidelity. We employ Efficient Low-Rank Adaptation (LoRA+) fine-tuning to enable precise domain specialization while maintaining the foundational capabilities of the pre-trained components. Extensive experimental validation on the SynthRAD2023 and BraTS2019 datasets demonstrates that ABS-Mamba outperforms state-of-the-art methods, delivering high-fidelity cross-modal synthesis that preserves anatomical semantics and structural details to enhance diagnostic accuracy in clinical applications. The code is available at https://github.com/gatina-yone/ABS-Mamba

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