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Guanyu Yang
Researcher at Southeast University
Publications - 109
Citations - 1702
Guanyu Yang is an academic researcher from Southeast University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 18, co-authored 80 publications receiving 1181 citations. Previous affiliations of Guanyu Yang include Leiden University Medical Center & Chinese Ministry of Education.
Papers
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Proceedings ArticleDOI
Automatic Segmentation of Kidney and Renal Tumor in CT Images Based on 3D Fully Convolutional Neural Network with Pyramid Pooling Module
Guanyu Yang,Guoqing Li,Tan Pan,Youyong Kong,Jiasong Wu,Huazhong Shu,Limin Luo,Jean-Louis Dillenseger,Jean-Louis Coatrieux,Lijun Tang,Xiaomei Zhu +10 more
TL;DR: This paper proposes a new method to perform a precise segmentation of kidney and renal tumor in CT angiography images which relies on a three-dimensional (3D) fully convolutional network (FCN) which combines a pyramid pooling module (PPM).
Journal ArticleDOI
Automatic coronary calcium scoring using noncontrast and contrast CT images.
TL;DR: The calcified lesions in the noncontrast CT images can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrast CT images with a very high accuracy.
Journal Article
Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets
Guanyu Yang,A. Broersen,Robert Petr,Pieter H. Kitslaar,Michiel A. de Graaf,Jeroen J Bax,Johan H. C. Reiber,Jouke Dijkstra +7 more
TL;DR: An automatic coronary tree labeling algorithm is developed for labeling the extracted branches with their anatomical names for CCTA datasets by means of a statistical coronary tree model.
Journal ArticleDOI
PV-LVNet: Direct left ventricle multitype indices estimation from 2D echocardiograms of paired apical views with deep neural networks.
TL;DR: A paired-views LV network (PV-LVNet) to automatically and directly estimate LV multitype indices from paired echo apical views is proposed and achieves high performance with accuracy up to 2.85mm mean absolute error and internal consistency up to 0.974 Cronbach's α for the cardiac indices estimation.
Proceedings ArticleDOI
A multiscale tracking algorithm for the coronary extraction in MSCT angiography.
TL;DR: The proposed approach makes use of a tracking algorithm of the vascular structure, combining a 3D geometric moment operator with a multiscale Hessian filter to estimate the vessel central axis location, its local diameter and orientation.