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.
Abstract (translated)
统一医学图像分割（UMIS）对于全面的解剖评估至关重要，但由于多尺度结构异质性而面临挑战。传统基于像素的方法由于缺乏对象级别的解剖洞察力和器官间关系建模，在处理形态复杂性和特征冲突方面遇到困难，从而限制了其在UMIS中的有效性。我们提出了一种名为Mamba Snake的新颖深度蛇框架，该框架通过状态空间建模增强以用于UMIS。 Mamba Snake将多轮廓演变视为分层的状态空间图集（atlas），有效地建模宏观的器官间拓扑关系和微观的轮廓细化。我们引入了针对蛇特定视觉状态空间模块——Mamba Evolution Block (MEB)，该模块利用有效的时空信息聚合，以适应性地改进复杂形态的细化。 能量图形状先验进一步确保在异质数据中的稳健长程轮廓演化。此外，还集成了双分类协同机制，同时优化检测和分割，减少UMIS中微结构的欠分割现象。 通过对五个临床数据集进行广泛的评估，证明了Mamba Snake具有优越的表现力，在Dice分数上相较于最先进方法平均提高了3%。
URL
https://arxiv.org/abs/2507.12760
