Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. I...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 38; H. 9; S. 2151 - 2164
Hauptverfasser:	Duan, Jinming, Bello, Ghalib, Schlemper, Jo, Bai, Wenjia, Dawes, Timothy J. W., Biffi, Carlo, de Marvao, Antonio, Doumoud, Georgia, O'Regan, Declan P., Rueckert, Daniel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Automation bi-ventricular CMR segmentation cardiac artifacts Cardiac Imaging Techniques - methods Computer applications Deep Learning Heart Heart - diagnostic imaging Humans Hypertension Image processing Image segmentation Imaging Imaging, Three-Dimensional - methods label fusion landmark localization Localization Machine learning Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging, Cine - methods multi-atlas segmentation non-rigid registration Pipelines Propagation Robustness (mathematics) Shape shape prior Three dimensional models Three-dimensional displays Ventricle
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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