Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity d...

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Veröffentlicht in:	IEEE transactions on medical imaging Jg. 38; H. 12; S. 2768 - 2778
Hauptverfasser:	Wang, Yi, Ni, Dong, Dou, Haoran, Hu, Xiaowei, Zhu, Lei, Yang, Xin, Xu, Ming, Qin, Jing, Heng, Pheng-Ann, Wang, Tianfu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	3D segmentation Artificial neural networks Attention mechanisms Biomedical imaging deep features feature pyramid network Image processing Image segmentation Medical imaging Modules Neural networks Prostate Semantics Shape Three-dimensional displays transrectal ultrasound Two dimensional displays Ultrasonic imaging Ultrasound
ISSN:	0278-0062, 1558-254X, 1558-254X
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Abstract	Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multi-level features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.
AbstractList	Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multi-level features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multi-level features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D. Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multi-level features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.
Author	Wang, Yi Ni, Dong Zhu, Lei Yang, Xin Heng, Pheng-Ann Hu, Xiaowei Dou, Haoran Qin, Jing Xu, Ming Wang, Tianfu
Author_xml	– sequence: 1 givenname: Yi orcidid: 0000-0002-8428-288X surname: Wang fullname: Wang, Yi organization: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China – sequence: 2 givenname: Dong orcidid: 0000-0002-9146-6003 surname: Ni fullname: Ni, Dong email: nidong@szu.edu.cn organization: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China – sequence: 3 givenname: Haoran orcidid: 0000-0001-8628-5489 surname: Dou fullname: Dou, Haoran organization: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China – sequence: 4 givenname: Xiaowei orcidid: 0000-0002-5708-7018 surname: Hu fullname: Hu, Xiaowei organization: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong – sequence: 5 givenname: Lei orcidid: 0000-0003-3871-663X surname: Zhu fullname: Zhu, Lei organization: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong – sequence: 6 givenname: Xin orcidid: 0000-0003-4653-6524 surname: Yang fullname: Yang, Xin organization: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong – sequence: 7 givenname: Ming surname: Xu fullname: Xu, Ming organization: Department of Medical Ultrasonics, First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-sen University, Guangzhou, China – sequence: 8 givenname: Jing orcidid: 0000-0002-2961-0860 surname: Qin fullname: Qin, Jing organization: Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong – sequence: 9 givenname: Pheng-Ann orcidid: 0000-0003-3055-5034 surname: Heng fullname: Heng, Pheng-Ann organization: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong – sequence: 10 givenname: Tianfu orcidid: 0000-0002-1248-1214 surname: Wang fullname: Wang, Tianfu organization: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
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Snippet	Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment...
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SubjectTerms	3D segmentation Artificial neural networks Attention mechanisms Biomedical imaging deep features feature pyramid network Image processing Image segmentation Medical imaging Modules Neural networks Prostate Semantics Shape Three-dimensional displays transrectal ultrasound Two dimensional displays Ultrasonic imaging Ultrasound
Title	Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
URI	https://ieeexplore.ieee.org/document/8698868 https://www.ncbi.nlm.nih.gov/pubmed/31021793 https://www.proquest.com/docview/2322888001 https://www.proquest.com/docview/2216284185
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