VesMamba：基于结构感知与尺度感知滤波的 Mamba 三维肺血管 CT 图像分割
VesMamba: 3D Pulmonary Vessel Segmentation from CT images via Mamba with Structural Perception and Scale-aware Filtering
将 Mamba 状态空间模型与动态空间注意力卷积结合，用于捕捉管状/树状结构的长程拓扑依赖，可迁移到视网膜血管、气道、神经纤维等细长结构分割任务；其尺度感知滤波与掩码约束解码器思路也可用于多尺度密集预测。
原文依据 we first devise a spatial-gated structural perception (SSP) module, which employs Mamba to efficiently capture long-range dependencies
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从 CT 图像中自动进行三维肺血管分割，对于提升肺血管相关疾病的早期筛查与评估至关重要。然而，由于血管具有复杂且呈树状的结构、尺度变化大，并且背景中存在高度相似的组织，该任务仍然极具挑战性。现有的分割模型要么无法充分捕捉长程结构依赖关系（这在血管分割中极为重要），要么在临床场景中受限于计算资源不足。本文提出 VesMamba，一种用于三维肺血管分割的新模型，全面应对上述挑战。具体而言，我们首先设计了一个空间门控结构感知（SSP）模块，利用 Mamba 高效捕捉长程依赖关系。在 SSP 中，我们设计了动态空间注意力卷积（DSAC），用于动态学习树状三维血管结构，为 Mamba 提供空间感知能力，从而更好地跟踪血管复杂的拓扑结构。其次，我们提出了一种创新的双向尺度感知滤波（BSF）模块，以增强编码器的表征能力，使模型能够在噪声下关注不同尺度的血管。此外，我们采用掩码约束解码器，通过高层掩码直接约束相邻低层解码器的推理，进一步提升分割的一致性与准确性。在公开的 Parse22 数据集和内部 Lung79 数据集上的大量实验表明，我们的方法能够取得优于当前最先进方法的性能。代码已开源，地址为 https://github.com/Lzpbright/VesMamba。
英文原文摘要 1715 字符
Automated 3D pulmonary vessel segmentation from CT images is crucial for improving early screening and assessment of pulmonary vessel related diseases. However, it remains an extremely challenging task due to the complex and tree-like structures of vessels, large scale-variations, and the existence of highly similar tissues in the background. Existing segmentation models either cannot sufficiently capture long-range structural dependencies, which are of great importance in vessel segmentation, or are constrained by insufficient computational resources in clinical settings. In this paper, we propose VesMamba, a novel model for 3D pulmonary vessel segmentation that comprehensively addresses these challenges. Specifically, we first devise a spatial-gated structural perception (SSP) module, which employs Mamba to efficiently capture long-range dependencies. In SSP, we design dynamic spatial attention convolutions (DSAC) for dynamically learning the tree-like 3D vessel structures, providing Mamba with the spatial perception capability to better track the complicated topologies of vessels. Second, we propose an innovative bidirectional scale-aware filter (BSF) module to strengthen the representation capability of the encoder, facilitating our model to focus on vessels of different scales under noise. Moreover, we apply a mask-constrained decoder to further improve segmentation consistency and accuracy, which constrains the inference of adjacent low-layer decoders directly by high-layer masks. Extensive experiments on the public Parse22 and internal Lung79 datasets demonstrate that our method can achieve better performance than SOTAs. Code is available at https://github.com/Lzpbright/VesMamba.