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

doi:10.1016/j.compmedimag.2022.102049

	Automated segmentation of normal and diseased coronary arteries-The ASOCA challenge
	Gharleghi, Ramtin 1; Adikari, Dona 2,3; Ellenberger, Katy 2,3; Ooi, Sze-Yuan 2,3; Ellis, Chris 4; Chen, Chung-Ming 5; Gao, Ruochen 6; He, Yuting 8; Hussain, Raabid 7; Lee, Chia-Yen 10; Li, Jun 6; Ma, Jun 11; Nie, Ziwei 12; Oliveira, Bruno 13,14,15; Qi, Yaolei 8; Skandarani, Youssef 7,9; Vilaca, Joao L.13; Wang, Xiyue 16; Yang, Sen 17; Sowmya, Arcot 18; Beier, Susann 1
	2022-04-01
发表期刊	COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
ISSN	0895-6111
卷号	97 页码:8
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.
关键词	Coronary arteries Image segmentation Machine learning
DOI	10.1016/j.compmedimag.2022.102049
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000787887200007
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18885
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.Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW, Australia 2.UNSW Sydney, Prince Wales Clin Sch Med, Sydney, NSW, Australia 3.Prince Wales Hosp, Dept Cardiol, Sydney, NSW, Australia 4.Auckland City Hosp, Auckland, New Zealand 5.Natl Taiwan Univ, Inst Biomed Engn, Taipei, Taiwan 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 7.Univ Burgundy, ImViA Lab, Dijon, France 8.Southeast Univ, Nanjing, Jiangsu, Peoples R China 9.CASIS Inc, Dijon, France 10.Natl United Univ, Dept Elect Engn, Miaoli, Miaoli County, Taiwan 11.Nanjing Univ Sci & Technol, Nanjing, Jiangsu, Peoples R China 12.Nanjing Univ, Nanjing, Jiangsu, Peoples R China 13.Polytech Inst Cavado & Ave, 2Ai Sch Technol, Barcelos, Portugal 14.Univ Minho, Sch Med, Life & Hlth Sci Res Inst ICVS, Braga, Portugal 15.Univ Minho, Algoritmi Ctr, Sch Engn, Guimaraes, Portugal 16.Sichuan Univ, Coll Comp Sci, Chengdu, Peoples R China 17.Sichuan Univ, Coll Biomed Engn, Chengdu, Peoples R China 18.Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia
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.