用状态空间模型提升医学图像分割，更好处理器官间关系与复杂形态。
Unified Medical Image Segmentation with State Space Modeling Snake
- 将多轮廓演化建模为分层状态空间，捕捉器官拓扑与轮廓细节。
- 在五个临床数据集上平均Dice提升3%，显著优于现有方法。
- 适合需要精准分割微小结构的医学影像分析场景。
统一医学图像分割（UMIS）对全面解剖评估至关重要，但受多尺度结构异质性挑战。传统基于像素的方法缺乏器官级解剖洞察与器官间关系建模能力，难以应对形态复杂性与特征冲突，限制了其在UMIS中的有效性。本文提出Mamba Snake，一种融合状态空间建模的新型深度蛇形框架。该方法将多轮廓演化建模为分层状态空间图谱，有效捕捉宏观器官拓扑关系与微观轮廓细化。引入专用于蛇形结构的状态空间模块——Mamba Evolution Block（MEB），实现时空信息高效聚合，自适应优化复杂形态。能量图形状先验增强长程轮廓演化的鲁棒性。此外，采用双分类协同机制，同步优化检测与分割，缓解微结构漏分割问题。在五个临床数据集上的广泛评估显示，Mamba Snake平均Dice分数相较最先进方法提升3%。
原文摘要 · Abstract (English)
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.
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