Q
Qing Cao
Researcher at Leiden University Medical Center
Publications - 11
Citations - 226
Qing Cao is an academic researcher from Leiden University Medical Center. The author has contributed to research in topics: Backtracking & Region growing. The author has an hindex of 5, co-authored 11 publications receiving 189 citations. Previous affiliations of Qing Cao include Southeast University.
Papers
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Journal ArticleDOI
Curve-Like Structure Extraction Using Minimal Path Propagation With Backtracking
Yang Chen,Yudong Zhang,Jian Yang,Qing Cao,Guanyu Yang,Jian Chen,Huazhong Shu,Limin Luo,Jean-Louis Coatrieux,Qianjing Feng +9 more
TL;DR: It is found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance.
Journal ArticleDOI
Dictionary learning based sinogram inpainting for CT sparse reconstruction
TL;DR: A sinogram inpainting method based on recently rising sparse representation technology is proposed to overcome the problem of reconstruction from undersampling projection data, and visual and numerical results validate the clinical potential of the proposed method.
Journal ArticleDOI
Automatic identification of coronary tree anatomy in coronary computed tomography angiography
Qing Cao,Alexander Broersen,Michiel A. de Graaf,Pieter H. Kitslaar,Guanyu Yang,Arthur J.H.A. Scholte,Boudewijn P. F. Lelieveldt,Johan H. C. Reiber,Jouke Dijkstra +8 more
TL;DR: The presented fully automatic labeling algorithm can identify and assign labels to the extracted coronary centerlines for both RD and LD circulations and got similar clinical risk scores as the two experts.
Proceedings ArticleDOI
Centerline constrained minimal path propagation for vessel extraction
Yang Chen,Qing Cao,Guanyu Yang,Huazhong Shu,Limin Luo,Christine Toumoulin,Jean-Louis Coatrieux +6 more
TL;DR: This paper proposes a Centerline Constrained Minimal Path Propagation (CCMPP) algorithm for vascular extraction and validates the utility of the proposed approach by using 3-D angiography data.
Journal ArticleDOI
A model-guided method for improving coronary artery tree extractions from CCTA images.
TL;DR: A model-guided method to automatically detect potential incorrect extractions and improve the extracted CATs to an average quality score of 93 guided by anatomical statistical models is described.