Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging

Purpose We propose an approach of 3D convolutional neural network to segment the prostate in MR images. Methods A 3D deep dense multi-path convolutional neural network that follows the framework of the encoder–decoder design is proposed. The encoder is built based upon densely connected layers that...

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Bibliographic Details
Published in:	International journal for computer assisted radiology and surgery Vol. 13; no. 11; pp. 1687 - 1696
Main Authors:	To, Minh Nguyen Nhat, Vu, Dang Quoc, Turkbey, Baris, Choyke, Peter L., Kwak, Jin Tae
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.11.2018 Springer Nature B.V
Subjects:	Artificial neural networks Computer Imaging Computer Science Convolution Datasets Ground truth Health Informatics Humans Image segmentation Imaging Imaging, Three-Dimensional - methods Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medicine Medicine & Public Health Neural networks Neural Networks (Computer) Original Article Pattern Recognition and Graphics Prostate Prostate - diagnostic imaging Radiology Surgery Vision Deep learning Dense connections Prostate segmentation Grouped convolution Magnetic resonance imaging
ISSN:	1861-6410, 1861-6429, 1861-6429
Online Access:	Get full text
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