Unified Medical Image Segmentation with State Space Modeling Snake

TL;DR

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.

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.

Figures (7)

• Figure 1: Illustration of the Mamba Snake segmentation pipeline.
• Figure 2: (a) Schematic of the hierarchical state space atlas. (b) Illustration of the Mamba Evolution Block (MEB). (c) Architecture of the contour evolution network. (d) Illustration of the circular convolution principle.
• Figure 3: Example energy maps generated from the mask images of five distinct datasets, illustrating how energy values vary based on shape, size, and spatial relationships within each dataset.
• Figure 4: The hidden state generation process for standard mamba and MEB. The standard Mamba restrict the central point to accessing only antecedent points, while MEB allows integrate of features from both antecedent and subsequent points.
• Figure 5: Qualitative comparison of segmentation results between Mamba Snake and other methods across five datasets.