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Automated segmentation of normal and diseased coronary arteries-The ASOCA challenge

文献类型：期刊论文

作者Gharleghi, Ramtin4; Adikari, Dona5,6; Ellenberger, Katy5,6; Ooi, Sze-Yuan5,6; Ellis, Chris7; Chen, Chung-Ming8; Gao, Ruochen9; He, Yuting3; Hussain, Raabid10; Lee, Chia-Yen12
刊名COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
出版日期2022-04-01
卷号97页码:8
ISSN号0895-6111
关键词Coronary arteries Image segmentation Machine learning
DOI10.1016/j.compmedimag.2022.102049
英文摘要Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and recon-structing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and ar-tefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new auto-mated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications.
资助项目Auckland Academic Health Alliance (AAHA) ; Auckland Medical Research Foundation (AMRF) ; Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER)[NORTE-010145-FEDER-000045] ; FCT ; European Social Found, through Programa Operacional Capital Humano (POCH)[SFRH/BD/136721/2018]
WOS研究方向Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:000787887200007
源URL[http://119.78.100.204/handle/2XEOYT63/18884]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Gharleghi, Ramtin
作者单位1.Sichuan Univ, Coll Biomed Engn, Chengdu, Peoples R China
2.Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia
3.Southeast Univ, Nanjing, Jiangsu, Peoples R China
4.Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW, Australia
5.UNSW Sydney, Prince Wales Clin Sch Med, Sydney, NSW, Australia
6.Prince Wales Hosp, Dept Cardiol, Sydney, NSW, Australia
7.Auckland City Hosp, Auckland, New Zealand
8.Natl Taiwan Univ, Inst Biomed Engn, Taipei, Taiwan
9.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
10.Univ Burgundy, ImViA Lab, Dijon, France
推荐引用方式
GB/T 7714		 Gharleghi, Ramtin,Adikari, Dona,Ellenberger, Katy,et al. Automated segmentation of normal and diseased coronary arteries-The ASOCA challenge[J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,2022,97:8.
APA Gharleghi, Ramtin.,Adikari, Dona.,Ellenberger, Katy.,Ooi, Sze-Yuan.,Ellis, Chris.,...&Beier, Susann.(2022).Automated segmentation of normal and diseased coronary arteries-The ASOCA challenge.COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,97,8.
MLA Gharleghi, Ramtin,et al."Automated segmentation of normal and diseased coronary arteries-The ASOCA challenge".COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 97(2022):8.

入库方式： OAI收割

来源：计算技术研究所

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