Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network

Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper,...

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Veröffentlicht in:	IEEE journal of translational engineering in health and medicine Jg. 7; S. 1 - 10
Hauptverfasser:	Du, Xiuquan, Yin, Susu, Tang, Renjun, Zhang, Yanping, Li, Shuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Architecture Automation Cardiovascular system CBV segmentation Coders Convolution Decoding deep learning Embedded systems encoder-decoder Encoders-Decoders Fires Image segmentation magnetic resonance images Magnetic resonance imaging Modules Myocardium Pixels Regional analysis Semantics Task analysis Wall thickness deep learning magnetic resonance images encoder-decoder CBV segmentation
ISSN:	2168-2372, 2168-2372
Online-Zugang:	Volltext
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