A Reverse Mamba Attention Network for Pathological Liver Segmentation
Journal:
arXiv
Published Date:
Feb 23, 2025
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.
