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

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

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

Afiliação

Yao T; College of Electronics and Information Engineering, Tongji University, Shanghai, 200092, China. Electronic address: 2150248@tongji.edu.cn.
Wang C; 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.
Wang X; School of Medicine, Tongji University, Shanghai, 200092, China. Electronic address: 1950332@tongji.edu.cn.
Li X; Departments of Cardiology and Nursing, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, China. Electronic address: 1841376@tongji.edu.cn.
Jiang Z; Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China. Electronic address: jiangzhaolei@xinhuamed.com.cn.
Qi P; College of Electronics and Information Engineering, Tongji University, Shanghai, 200092, China. Electronic address: pqi@tongji.edu.cn.

Comput Biol Med ; 166: 107540, 2023 Oct 04.

Article em En | MEDLINE | ID: mdl-37806060

Palavras-chave

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

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Comput Biol Med Ano de publicação: 2023 Tipo de documento: Article

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