Temporal consistency-aware network for renal artery segmentation in X-ray angiography.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 2, 2025

Abstract

PURPOSE: Accurate segmentation of renal arteries from X-ray angiography videos is crucial for evaluating renal sympathetic denervation (RDN) procedures but remains challenging due to dynamic changes in contrast concentration and vessel morphology across frames. The purpose of this study is to propose TCA-Net, a deep learning model that improves segmentation consistency by leveraging local and global contextual information in angiography videos.

Authors

  • Botao Yang
    School of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan Road, Shanghai, 200030, China.
  • Chunming Li
    Department of Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Simone Fezzi
    Department of Medicine, University of Verona, Viale dell'Università, Verona, 37135, Italy.
  • Zehao Fan
  • Runguo Wei
    School of Biomedical Engineering, Shanghai Jiao Tong University, HuaShan Road, Shanghai, 200030, China.
  • Yankai Chen
    School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
  • Domenico Tavella
    Department of Medicine, University of Verona, Viale dell'Università, Verona, 37135, Italy.
  • Flavio L Ribichini
    Department of Medicine, University of Verona, Viale dell'Università, Verona, 37135, Italy.
  • Su Zhang
  • Faisal Sharif
    Department of Cardiology, University Hospital Galway, Old Dublin Road, Galway, H91 D79N, Ireland.
  • Shengxian Tu
    Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University.

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