A Reverse Mamba Attention Network for Pathological Liver Segmentation

• URL: http://arxiv.org/abs/2502.18232v2
• Date: Wed, 05 Mar 2025 19:18:00 GMT
• Title: A Reverse Mamba Attention Network for Pathological Liver Segmentation
• Authors: Jun Zeng, Debesh Jha, Ertugrul Aktas, Elif Keles, Alpay Medetalibeyoglu, Matthew Antalek, Robert Lewandowski, Daniela Ladner, Amir A. Borhani, Gorkem Durak, Ulas Bagci,
• Abstract summary: We present RMA-Mamba, a novel architecture that advances the capabilities of vision state space models.<n>By integrating Vision Mamba (VMamba) with RMA's targeted feature refinement, our architecture achieves superior feature learning across multiple scales.<n>We demonstrate RMA-Mamba's effectiveness in the challenging domain of pathological liver segmentation.
• Score: 10.500870300348025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present RMA-Mamba, a novel architecture that advances the capabilities of vision state space models through a specialized reverse mamba attention module (RMA). The key innovation lies in RMA-Mamba's ability to capture long-range dependencies while maintaining precise local feature representation through its hierarchical processing pipeline. By integrating Vision Mamba (VMamba)'s efficient sequence modeling with RMA's targeted feature refinement, our architecture achieves superior feature learning across multiple scales. This dual-mechanism approach enables robust handling of complex morphological patterns while maintaining computational efficiency. We demonstrate RMA-Mamba's effectiveness in the challenging domain of pathological liver segmentation (from both CT and MRI), where traditional segmentation approaches often fail due to tissue variations. When evaluated on a newly introduced cirrhotic liver dataset (CirrMRI600+) of T2-weighted MRI scans, RMA-Mamba achieves the state-of-the-art performance with a Dice coefficient of 92.08%, mean IoU of 87.36%, and recall of 92.96%. The architecture's generalizability is further validated on the cancerous liver segmentation from CT scans (LiTS: Liver Tumor Segmentation dataset), yielding a Dice score of 92.9% and mIoU of 88.99%. Our code is available for public: https://github.com/JunZengz/RMAMamba.

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