NDLI: Invert-U-Net DNN segmentation model for MRI cardiac left ventricle segmentation

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Invert-U-Net DNN segmentation model for MRI cardiac left ventricle segmentation

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Invert-U-Net DNN segmentation model for MRI cardiac left ventricle segmentation

Content Provider	IET Digital Library
Author	Cong, Chao Zhang, Hongmin
Abstract	In this study, a deeply supervised end-to-end model is presented for fully automated segmentation for cardiac magnetic resonance imaging (MRI) images. Firstly, the mechanism of deep neural network (DNN)-based segmentation is discussed with the relationship of channels and their distribution in network in depth. Following this idea, a U-Net-based model, namely an invert-U-Net model is presented with an innovative filter number structure. Based on the invert-U-Net model, the experiment is carefully designed, and the hyper-parameters are considerately arranged. Finally, the model is applied and evaluated using Sunnybrook MR datasets from the MICCAI 2009 LV segmentation challenge and the experimental result shows that it outperforms the state-of-the-art methods.
Starting Page	1463
Ending Page	1467
Page Count	5
Volume Number	2018
e-ISSN	20513305
Issue Number	Issue 16, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2018/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8302
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-08-16
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Cardiac Magnetic Resonance Imaging Image Cardiology Channel Distribution Computer Vision And Image Processing Technique Deep Neural Network-based Segmentation Deeply Supervised End-to-end Model Fully Automated Segmentation Image Filtering Image Segmentation Innovative Filter Number Structure Invert-U-Net DNN Segmentation Model Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy MICCAI 2009 LV Segmentation Challenge MRI MRI Cardiac Left Ventricle Segmentation Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Spectroscopy Sunnybrook MR Datasets
Content Type	Text
Resource Type	Article

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