SPIRONet: Spatial-Frequency Learning and Graph-based Channel Interaction Network for Vessel Segmentation

Automatic vessel segmentation plays a pivotal role in the development of next-generation interventional navigation systems for surgical robotics. However, current approaches still suffer from suboptimal segmentation performance under challenging intraoperative conditions, such as low-signal-to-noise ratio (SNR), small or slender vessels, and strong interference. In this study, a novel spatial-frequency learning and graph-based channel interaction network (SPIRONet) is proposed to address the above issues. To address low-SNR vessel appearance and small or slender branches, dual spatial-frequency encoders are utilized, where the frequency encoder captures global vessel continuity that is less affected by local noise fluctuations, while the spatial encoder preserves fine vessel details. A cross-attention fusion module is further introduced to adaptively integrate this complementary spatial and frequency information. Moreover, to suppress interference from non-target vessels and vessel-like structures, a graph-based channel interaction module is designed to model channel-wise correlations, enhancing consistent vessel-related responses while suppressing task-irrelevant activations. Extensive experimental results on five challenging datasets demonstrate that the proposed method achieves competitive and consistently strong performance compared with existing methods. For example, SPIRONet achieves IoU improvements of +0.87%, +0.52%, +0.23%, +1.39%, and +2.22% over the strongest competing methods on CADSA, CAXF, DCA1, XCAD, and ARCADE, respectively. Moreover, SPIRONet achieves an inference speed of 21 FPS with a 512x512 input size, meeting the real-time requirements of interventional scenarios (6-12 FPS). These promising results indicate SPIRONet's potential for integration into interventional navigation systems. Code is available at https://github.com/Dxhuang-CASIA/SPIRONet.