Enhancing percutaneous coronary intervention with heuristic path planning and deep-learning-based vascular segmentation.

Journal: Computers in biology and medicine
Published Date:

Abstract

Percutaneous coronary intervention (PCI) is a minimally invasive technique for treating vascular diseases. PCI requires precise and real-time visualization and guidance to ensure surgical safety and efficiency. Existing mainstream guiding methods rely on hemodynamic parameters. However, these methods are less intuitive than images and pose some challenges to the decision-making of cardiologists. This paper proposes a novel PCI guiding assistance system by combining a novel vascular segmentation network and a heuristic intervention path planning algorithm, providing cardiologists with clear and visualized information. A dataset of 1077 DSA images from 288 patients is also collected in clinical practice. A Likert Scale is also designed to evaluate system performance in user experiments. Results of user experiments demonstrate that the system can generate satisfactory and reasonable paths for PCI. Our proposed method outperformed the state-of-the-art baselines based on three metrics (Jaccard: 0.4091, F1: 0.5626, Accuracy: 0.9583). The proposed system can effectively assist cardiologists in PCI by providing a clear segmentation of vascular structures and optimal real-time intervention paths, thus demonstrating great potential for robotic PCI autonomy.

Authors

  • Tianliang Yao
    College of Electronics and Information Engineering, Tongji University, Shanghai, 200092, China. Electronic address: 2150248@tongji.edu.cn.
  • Chengjia Wang
  • Xinyi Wang
    School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Zhaolei Jiang
    Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China. Electronic address: jiangzhaolei@xinhuamed.com.cn.
  • Peng Qi