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

Tianliang Yao, Chengjia Wang, Xinyi Wang, Xiang Li, Zhaolei Jiang, Peng Qi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number107540
JournalComputers in Biology and Medicine
Early online date6 Oct 2023
Publication statusPublished - Nov 2023


  • Deep learning
  • Path planning
  • Percutaneous coronary intervention
  • Surgery assistance system
  • Vessel segmentation

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


Dive into the research topics of 'Enhancing percutaneous coronary intervention with heuristic path planning and deep-learning-based vascular segmentation'. Together they form a unique fingerprint.

Cite this