Deep convolutional neural network for prostate MR segmentation

Zhiqiang Tian; Lizhi Liu; Baowei Fei

doi:10.1117/12.2254621

Deep convolutional neural network for prostate MR segmentation

Zhiqiang Tian
, Lizhi Liu
, Baowei Fei

Emory University
Emory University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

23 引用（Scopus）

摘要

Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%±3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.

源语言	英语
主期刊名	Medical Imaging 2017
主期刊副标题	Image-Guided Procedures, Robotic Interventions, and Modeling
编辑	Robert J. Webster, Baowei Fei
出版商	SPIE
ISBN（电子版）	9781510607156
DOI	https://doi.org/10.1117/12.2254621
出版状态	已出版 - 2017
已对外发布	是
活动	Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling - Orlando, 美国期限: 14 2月 2017 → 16 2月 2017

出版系列

姓名	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
卷	10135
ISSN（印刷版）	1605-7422

会议

会议	Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
国家/地区	美国
市	Orlando
时期	14/02/17 → 16/02/17

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

10.1117/12.2254621

其它文件与链接

链接到 Scopus 的出版物

引用此

@inproceedings{2dda28829e774f3d9ed7d7b255fca984,

title = "Deep convolutional neural network for prostate MR segmentation",

abstract = "Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3\%±3.2\% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.",

keywords = "Convolutional neural network, Deep learning, Magnetic resonance imaging (MRI), Prostate segmentation",

author = "Zhiqiang Tian and Lizhi Liu and Baowei Fei",

note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 14-02-2017 Through 16-02-2017",

year = "2017",

doi = "10.1117/12.2254621",

language = "英语",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Webster, \{Robert J.\} and Baowei Fei",

booktitle = "Medical Imaging 2017",

}

Tian, Z, Liu, L & Fei, B 2017, Deep convolutional neural network for prostate MR segmentation. 在 RJ Webster & B Fei (编辑), Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling., 101351L, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 卷 10135, SPIE, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, Orlando, 美国, 14/02/17. https://doi.org/10.1117/12.2254621

Deep convolutional neural network for prostate MR segmentation. / Tian, Zhiqiang; Liu, Lizhi; Fei, Baowei.
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling. 编辑 / Robert J. Webster; Baowei Fei. SPIE, 2017. 101351L (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; 卷 10135).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Deep convolutional neural network for prostate MR segmentation

AU - Tian, Zhiqiang

AU - Liu, Lizhi

AU - Fei, Baowei

PY - 2017

Y1 - 2017

N2 - Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%±3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.

AB - Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%±3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.

KW - Convolutional neural network

KW - Deep learning

KW - Magnetic resonance imaging (MRI)

KW - Prostate segmentation

UR - https://www.scopus.com/pages/publications/85020421134

U2 - 10.1117/12.2254621

DO - 10.1117/12.2254621

M3 - 会议稿件

AN - SCOPUS:85020421134

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2017

A2 - Webster, Robert J.

A2 - Fei, Baowei

PB - SPIE

T2 - Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling

Y2 - 14 February 2017 through 16 February 2017

ER -

Deep convolutional neural network for prostate MR segmentation

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此