3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images

Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR) imaging has critical importance for the diagnosis and treatment of prostate diseases, especially prostate cancer. Although many automated segmentation approaches, including those based on deep learning have been p...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 39; H. 2; S. 447 - 457
Hauptverfasser:	Jia, Haozhe, Xia, Yong, Song, Yang, Zhang, Donghao, Huang, Heng, Zhang, Yanning, Cai, Weidong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	adversarial training Algorithms Anisotropy Artificial neural networks Biomedical imaging Coders Convolution Decoding Deep learning Encoders-Decoders Glands Ground truth Humans Image classification Image processing Image Processing, Computer-Assisted - methods Image segmentation Imaging, Three-Dimensional - methods Localization Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medical treatment Neural networks Neural Networks, Computer Prostate Prostate - diagnostic imaging Prostate cancer Prostate segmentation Spatial resolution Three-dimensional displays Workflow
ISSN:	0278-0062, 1558-254X, 1558-254X
Online-Zugang:	Volltext
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