A Reverse Mamba Attention Network for Pathological Liver Segmentation

TL;DR

The paper introduces RMA-Mamba, a novel neural network architecture that combines reverse mamba attention with vision Mamba for improved pathological liver segmentation across CT and MRI scans, achieving state-of-the-art results.

Contribution

It presents a new architecture integrating reverse mamba attention with vision Mamba, enhancing long-range dependency capture and local feature refinement for medical image segmentation.

Findings

Achieved a Dice coefficient of 92.08% on cirrhotic liver MRI dataset.

Attained a Dice score of 92.9% on CT liver tumor segmentation.

Demonstrated superior performance over traditional methods.

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.