Unified Medical Image Segmentation with State Space Modeling Snake

TL;DR

The paper introduces Mamba Snake, a novel deep snake framework utilizing state space modeling to improve unified medical image segmentation by capturing inter-organ relationships and complex morphologies, achieving superior accuracy.

Contribution

It presents a new hierarchical state space modeling approach with a specialized vision module and dual-classification mechanism for enhanced UMIS performance.

Findings

Achieved an average Dice score improvement of 3% over existing methods.

Effectively models inter-organ relationships and complex morphologies.

Demonstrated superior performance across five clinical datasets.

Abstract

Unified Medical Image Segmentation (UMIS) is critical for comprehensive anatomical assessment but faces challenges due to multi-scale structural heterogeneity. Conventional pixel-based approaches, lacking object-level anatomical insight and inter-organ relational modeling, struggle with morphological complexity and feature conflicts, limiting their efficacy in UMIS. We propose Mamba Snake, a novel deep snake framework enhanced by state space modeling for UMIS. Mamba Snake frames multi-contour evolution as a hierarchical state space atlas, effectively modeling macroscopic inter-organ topological relationships and microscopic contour refinements. We introduce a snake-specific vision state space module, the Mamba Evolution Block (MEB), which leverages effective spatiotemporal information aggregation for adaptive refinement of complex morphologies. Energy map shape priors further ensure robust long-range contour evolution in heterogeneous data. Additionally, a dual-classification synergy mechanism is incorporated to concurrently optimize detection and segmentation, mitigating under-segmentation of microstructures in UMIS. Extensive evaluations across five clinical datasets reveal Mamba Snake's superior performance, with an average Dice improvement of 3\% over state-of-the-art methods.