![](/images/graphics-bg.png)
Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect
Joint Authors
Zhu, Qikui
Du, Bo
Turkbey, Baris
Yan, Pingkun
Choyke, Peter L.
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Segmentation of the prostate from Magnetic Resonance Imaging (MRI) plays an important role in prostate cancer diagnosis.
However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging.
In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation.
However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume.
As a result, this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information.
To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation.
In addition to utilizing the intraslice contexts and features, the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation.
The experimental results show that the proposed approach achieved significant segmentation improvement compared to other reported methods.
American Psychological Association (APA)
Zhu, Qikui& Du, Bo& Turkbey, Baris& Choyke, Peter L.& Yan, Pingkun. 2018. Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect. Complexity،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1134035
Modern Language Association (MLA)
Zhu, Qikui…[et al.]. Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect. Complexity No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1134035
American Medical Association (AMA)
Zhu, Qikui& Du, Bo& Turkbey, Baris& Choyke, Peter L.& Yan, Pingkun. Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect. Complexity. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1134035
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134035