标题： SWinMamba：用于血管分割的蛇形窗口状态空间模型 显示英文标题

标题： SWinMamba: Serpentine Window State Space Model for Vascular Segmentation

摘要： 血管分割在医学图像中对于疾病诊断和手术导航至关重要。 然而，由于血管结构细长且先验建模不足，分割后的血管结构往往不连续。 本文提出了一种新颖的蛇形窗口马尔可夫（SWinMamba）以实现准确的血管分割。 所提出的SWinMamba通过将蛇形窗口序列引入双向状态空间模型，创新性地建模了细长血管结构的连续性。 蛇形窗口序列通过自适应引导全局视觉上下文建模到血管结构，实现了高效的特征捕捉。 具体来说，蛇形窗口分词器（SWToken）使用重叠的蛇形窗口序列自适应地分割输入图像，为血管结构建模提供了灵活的感受野（RFs）。 双向聚合模块（BAM）整合感受野中的连贯局部特征，用于血管连续性表示。 此外，设计了具有空间-频率融合单元（SFFU）的双域学习，以增强血管结构的特征表示。 在三个具有挑战性的数据集上的大量实验表明，所提出的SWinMamba在完整且连接的血管方面表现出优越的性能。 显示英文摘要

摘要： Vascular segmentation in medical images is crucial for disease diagnosis and surgical navigation. However, the segmented vascular structure is often discontinuous due to its slender nature and inadequate prior modeling. In this paper, we propose a novel Serpentine Window Mamba (SWinMamba) to achieve accurate vascular segmentation. The proposed SWinMamba innovatively models the continuity of slender vascular structures by incorporating serpentine window sequences into bidirectional state space models. The serpentine window sequences enable efficient feature capturing by adaptively guiding global visual context modeling to the vascular structure. Specifically, the Serpentine Window Tokenizer (SWToken) adaptively splits the input image using overlapping serpentine window sequences, enabling flexible receptive fields (RFs) for vascular structure modeling. The Bidirectional Aggregation Module (BAM) integrates coherent local features in the RFs for vascular continuity representation. In addition, dual-domain learning with Spatial-Frequency Fusion Unit (SFFU) is designed to enhance the feature representation of vascular structure. Extensive experiments on three challenging datasets demonstrate that the proposed SWinMamba achieves superior performance with complete and connected vessels.