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

doi:10.1016/j.compbiomed.2023.107540

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

Comput Biol Med. 2023 Oct 4:166:107540. doi: 10.1016/j.compbiomed.2023.107540. Online ahead of print.

Authors

Tianliang Yao¹, Chengjia Wang², Xinyi Wang³, Xiang Li⁴, Zhaolei Jiang⁵, Peng Qi⁶

Affiliations

¹ College of Electronics and Information Engineering, Tongji University, Shanghai, 200092, China. Electronic address: 2150248@tongji.edu.cn.
² School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, EH14 4AP, United Kingdom; BHF Centre for Cardiovascular Science,University of Edinburgh, Edinburgh, EH16 4TJ, United Kingdom. Electronic address: Chengjia.Wang@hw.ac.uk.
³ School of Medicine, Tongji University, Shanghai, 200092, China. Electronic address: 1950332@tongji.edu.cn.
⁴ Departments of Cardiology and Nursing, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China. Electronic address: 1841376@tongji.edu.cn.
⁵ Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China. Electronic address: jiangzhaolei@xinhuamed.com.cn.
⁶ College of Electronics and Information Engineering, Tongji University, Shanghai, 200092, China. Electronic address: pqi@tongji.edu.cn.

PMID: 37806060
DOI: 10.1016/j.compbiomed.2023.107540

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.

Keywords: Deep learning; Path planning; Percutaneous coronary intervention; Surgery assistance system; Vessel segmentation.