mlsanityData to 7 Oct 2026

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

arXiv 2406.19749 ↗First posted 28 Jun 2024eess.IV cs.CVcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 77 references that are in the map. First posted on arXiv 28 Jun 2024.

Builds on

31 of 77 references.

References · 31

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2024CAS-GAN for Contrast-free Angiography Synthesiscited by 2
  2. 2025Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutionscited by 2
  3. 2025VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentationcited by 2
  4. 2024Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correctioncited by 0
  5. 2025REASON: Probability map-guided dual-branch fusion framework for gastric content assessmentcited by 0
  6. 2026Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysiscited by 0