SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes

• URL: http://arxiv.org/abs/2508.12410v2
• Date: Tue, 19 Aug 2025 06:05:00 GMT
• Title: SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
• Authors: Jun Zeng, Yannan Huang, Elif Keles, Halil Ertugrul Aktas, Gorkem Durak, Nikhil Kumar Tomar, Quoc-Huy Trinh, Deepak Ranjan Nayak, Ulas Bagci, Debesh Jha,
• Abstract summary: Liver Cirrhosis plays a critical role in the prognosis of chronic liver disease.<n>Existing methods underutilize the spatial details in MRI data.<n>We introduce a novel Mamba-based network, designed to model the spatial relationships within the complex anatomical structures of MRI volumes.
• Score: 10.398312170809222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Liver Cirrhosis plays a critical role in the prognosis of chronic liver disease. Early detection and timely intervention are critical in significantly reducing mortality rates. However, the intricate anatomical architecture and diverse pathological changes of liver tissue complicate the accurate detection and characterization of lesions in clinical settings. Existing methods underutilize the spatial anatomical details in volumetric MRI data, thereby hindering their clinical effectiveness and explainability. To address this challenge, we introduce a novel Mamba-based network, SRMA-Mamba, designed to model the spatial relationships within the complex anatomical structures of MRI volumes. By integrating the Spatial Anatomy-Based Mamba module (SABMamba), SRMA-Mamba performs selective Mamba scans within liver cirrhotic tissues and combines anatomical information from the sagittal, coronal, and axial planes to construct a global spatial context representation, enabling efficient volumetric segmentation of pathological liver structures. Furthermore, we introduce the Spatial Reverse Attention module (SRMA), designed to progressively refine cirrhotic details in the segmentation map, utilizing both the coarse segmentation map and hierarchical encoding features. Extensive experiments demonstrate that SRMA-Mamba surpasses state-of-the-art methods, delivering exceptional performance in 3D pathological liver segmentation. Our code is available for public: https://github.com/JunZengz/SRMA-Mamba.

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