Prior skeleton based online deep reinforcement learning for coronary artery centerline extraction

Author:

Fu Zeyu12ORCID,Fu Zhuang12,Fang Zi12,Wang Zehao12,Fei Jian3456,Xie Rongli3,Han Hui7

Affiliation:

1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China

3. Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

4. Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China

5. State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University, Shanghai, China

6. Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

7. Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Abstract

Coronary centerline extraction is an essential technique for X-ray coronary angiography (XCA) image analysis, which provides qualitative and quantitative guidance for percutaneous coronary intervention (PCI). In this paper, an online deep reinforcement learning method for coronary centerline extraction is proposed based on the prior vascular skeleton. Firstly, with XCA image preprocessing (foreground extraction and vessel segmentation) results, the improved ZhangSuen image thinning algorithm is used to rapidly extract the preliminary vascular skeleton network. On this basis, according to the spatial-temporal and morphological continuity of the angiography image sequence, the connectivity of different branches is determined using k-means clustering, and the vessel segments are then grouped, screened, and reconnected to obtain the aorta and its major branches. Finally, using the previous results as prior information, an online Deep Q-Network (DQN) reinforcement learning method is proposed to optimize each branch simultaneously. It comprehensively considers grayscale intensity and eigenvector continuity to achieve the combination of data-driven and model-driven without pre-training. Experimental results on clinical images and the third-party dataset demonstrate that the proposed method can accurately extract, restructure, and optimize the centerline of XCA images with a higher overall accuracy than the existing state-of-the-art methods.

Funder

Medical-engineering Cross Projects of SJTU

National Natural Science Foundation of China

Publisher

SAGE Publications

Subject

Mechanical Engineering,General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Constructing Coronary Artery Vessel Segmentation Based on Deep Learning Models and Image Processing;Proceedings of the 2024 8th International Conference on Medical and Health Informatics;2024-05-17

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