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Huang X, Bajaj R, Li Y, Ye X, Lin J, Pugliese F, Ramasamy A, Gu Y, Wang Y, Torii R, Dijkstra J, Zhou H, Bourantas CV, Zhang Q. POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation. Med Image Anal 2023; 89:102922. [PMID: 37598605 DOI: 10.1016/j.media.2023.102922] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 07/06/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.
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Affiliation(s)
- Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK; School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, Zhejiang, China
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yilong Li
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK
| | - Xin Ye
- Zhejiang Provincial People's Hospital, 270 West Xueyuan Road, Wenzhou, Zhejiang, China
| | - Ji Lin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yue Gu
- Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou, China
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, China
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | | | - Huiyu Zhou
- School of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK.
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Liu Z, Yang J, Chen Y. The Chinese Experience of Imaging in Cardiac Intervention: A Bird's Eye Review. J Thorac Imaging 2022; 37:374-384. [PMID: 36162061 DOI: 10.1097/rti.0000000000000680] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Recent scientific and technological advances have greatly contributed to the development of medical imaging that could enable specific functions. It has become the primary focus of cardiac intervention in preoperative assessment, intraoperative guidance, and postoperative follow-up. This review provides a contemporary overview of the Chinese experience of imaging in cardiac intervention in recent years.
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Affiliation(s)
- Zinuan Liu
- Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital
- Medical School of Chinese PLA, Beijing, P.R. China
| | - Junjie Yang
- Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital
| | - Yundai Chen
- Senior Department of Cardiology, The Sixth Medical Center of PLA General Hospital
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