Automatic vessel segmentation is paramount for developing next-generation
interventional navigation systems. However, current approaches suffer from
suboptimal segmentation performances due to significant challenges in
intraoperative images (i.e., low signal-to-noise ratio, small or slender
vessels, and strong interference). In this paper, a novel spatial-frequency
learning and topological channel interaction network (SPIRONet) is proposed to
address the above issues. Specifically, dual encoders are utilized to
comprehensively capture local spatial and global frequency vessel features.
Then, a cross-attention fusion module is introduced to effectively fuse spatial
and frequency features, thereby enhancing feature discriminability.
Furthermore, a topological channel interaction module is designed to filter out
task-irrelevant responses based on graph neural networks. Extensive
experimental results on several challenging datasets (CADSA, CAXF, DCA1, and
XCAD) demonstrate state-of-the-art performances of our method. Moreover, the
inference speed of SPIRONet is 21 FPS with a 512x512 input size, surpassing
clinical real-time requirements (6~12FPS). These promising outcomes indicate
SPIRONet's potential for integration into vascular interventional navigation
systems. Code is available at this https URL

提出了一种新颖的空间频率学习和拓扑通道交互网络（SPIRONet），旨在解决当前自动血管分割方法在低信噪比、细小的血管和强干扰等显著挑战下表现不佳的问题。通过使用双编码器全面捕获局部空间和全局频率血管特征，引入交叉注意力融合模块来有效融合空间和频率特征，以增强特征的可辨识度；此外，设计了一个拓扑通道交互模块，基于图神经网络来过滤掉与任务无关的响应。在多个具有挑战性的数据集上的广泛实验结果（CADSA，CAXF，DCA1 和 XCAD）显示出我们方法的最先进性能。此外，SPIRONet 的推理速度为 512x512 输入尺寸下的 21FPS，超过了临床实时要求（6〜12FPS）。这些有希望的成果表明 SPIRONet 在血管介入导航系统中的潜力。代码可在此 URL 找到。