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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Published in:	IEEE transactions on medical imaging Vol. 39; no. 2; pp. 447 - 457
Main Authors:	Jia, Haozhe, Xia, Yong, Song, Yang, Zhang, Donghao, Huang, Heng, Zhang, Yanning, Cai, Weidong
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.02.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
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Abstract	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 proposed, the segmentation performance still has room for improvement due to the large variability in image appearance, imaging interference, and anisotropic spatial resolution. In this paper, we propose the 3D adversarial pyramid anisotropic convolutional deep neural network (3D APA-Net) for prostate segmentation in MR images. This model is composed of a generator (i.e., 3D PA-Net) that performs image segmentation and a discriminator (i.e., a six-layer convolutional neural network) that differentiates between a segmentation result and its corresponding ground truth. The 3D PA-Net has an encoder-decoder architecture, which consists of a 3D ResNet encoder, an anisotropic convolutional decoder, and multi-level pyramid convolutional skip connections. The anisotropic convolutional blocks can exploit the 3D context information of the MR images with anisotropic resolution, the pyramid convolutional blocks address both voxel classification and gland localization issues, and the adversarial training regularizes 3D PA-Net and thus enables it to generate spatially consistent and continuous segmentation results. We evaluated the proposed 3D APA-Net against several state-of-the-art deep learning-based segmentation approaches on two public databases and the hybrid of the two. Our results suggest that the proposed model outperforms the compared approaches on three databases and could be used in a routine clinical workflow.
AbstractList	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 proposed, the segmentation performance still has room for improvement due to the large variability in image appearance, imaging interference, and anisotropic spatial resolution. In this paper, we propose the 3D adversarial pyramid anisotropic convolutional deep neural network (3D APA-Net) for prostate segmentation in MR images. This model is composed of a generator (i.e., 3D PA-Net) that performs image segmentation and a discriminator (i.e., a six-layer convolutional neural network) that differentiates between a segmentation result and its corresponding ground truth. The 3D PA-Net has an encoder-decoder architecture, which consists of a 3D ResNet encoder, an anisotropic convolutional decoder, and multi-level pyramid convolutional skip connections. The anisotropic convolutional blocks can exploit the 3D context information of the MR images with anisotropic resolution, the pyramid convolutional blocks address both voxel classification and gland localization issues, and the adversarial training regularizes 3D PA-Net and thus enables it to generate spatially consistent and continuous segmentation results. We evaluated the proposed 3D APA-Net against several state-of-the-art deep learning-based segmentation approaches on two public databases and the hybrid of the two. Our results suggest that the proposed model outperforms the compared approaches on three databases and could be used in a routine clinical workflow. 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 proposed, the segmentation performance still has room for improvement due to the large variability in image appearance, imaging interference, and anisotropic spatial resolution. In this paper, we propose the 3D adversarial pyramid anisotropic convolutional deep neural network (3D APA-Net) for prostate segmentation in MR images. This model is composed of a generator (i.e., 3D PA-Net) that performs image segmentation and a discriminator (i.e., a six-layer convolutional neural network) that differentiates between a segmentation result and its corresponding ground truth. The 3D PA-Net has an encoder-decoder architecture, which consists of a 3D ResNet encoder, an anisotropic convolutional decoder, and multi-level pyramid convolutional skip connections. The anisotropic convolutional blocks can exploit the 3D context information of the MR images with anisotropic resolution, the pyramid convolutional blocks address both voxel classification and gland localization issues, and the adversarial training regularizes 3D PA-Net and thus enables it to generate spatially consistent and continuous segmentation results. We evaluated the proposed 3D APA-Net against several state-of-the-art deep learning-based segmentation approaches on two public databases and the hybrid of the two. Our results suggest that the proposed model outperforms the compared approaches on three databases and could be used in a routine clinical workflow.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 proposed, the segmentation performance still has room for improvement due to the large variability in image appearance, imaging interference, and anisotropic spatial resolution. In this paper, we propose the 3D adversarial pyramid anisotropic convolutional deep neural network (3D APA-Net) for prostate segmentation in MR images. This model is composed of a generator (i.e., 3D PA-Net) that performs image segmentation and a discriminator (i.e., a six-layer convolutional neural network) that differentiates between a segmentation result and its corresponding ground truth. The 3D PA-Net has an encoder-decoder architecture, which consists of a 3D ResNet encoder, an anisotropic convolutional decoder, and multi-level pyramid convolutional skip connections. The anisotropic convolutional blocks can exploit the 3D context information of the MR images with anisotropic resolution, the pyramid convolutional blocks address both voxel classification and gland localization issues, and the adversarial training regularizes 3D PA-Net and thus enables it to generate spatially consistent and continuous segmentation results. We evaluated the proposed 3D APA-Net against several state-of-the-art deep learning-based segmentation approaches on two public databases and the hybrid of the two. Our results suggest that the proposed model outperforms the compared approaches on three databases and could be used in a routine clinical workflow.
Author	Xia, Yong Zhang, Yanning Huang, Heng Jia, Haozhe Song, Yang Cai, Weidong Zhang, Donghao
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References	ref13 ref34 ref12 ref37 ref15 ref36 ref14 ref31 ref33 ref11 ref32 ref10 ref1 ref39 ref17 ref38 ref16 luc (ref30) 2016 ref19 liu (ref24) 2018 ref45 ref26 ref20 ref41 sciolla (ref23) 2017 ref22 ref21 ref43 goodfellow (ref35) 2014 ref28 ref27 (ref2) 2012 yu (ref25) 2017 ref29 ref8 ref7 ronneberger (ref18) 2015 ref9 ref4 kinga (ref44) 2015 ref3 çiçek (ref46) 2016 ref6 ref5 ref40 (ref42) 2013
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SubjectTerms	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
Title	3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images
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