Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect
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, appro...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4185279 |
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| author | Qikui Zhu Bo Du Baris Turkbey Peter Choyke Pingkun Yan |
| author_facet | Qikui Zhu Bo Du Baris Turkbey Peter Choyke Pingkun Yan |
| author_sort | Qikui Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-76f3f6e387454eb9b30936143af905fa |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-76f3f6e387454eb9b30936143af905fa2025-02-03T06:14:12ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/41852794185279Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks AspectQikui Zhu0Bo Du1Baris Turkbey2Peter Choyke3Pingkun Yan4School of Computer, Wuhan University, Wuhan, ChinaSchool of Computer, Wuhan University, Wuhan, ChinaNational Cancer Institute, National Institutes of Health, Bethesda, MD, USANational Cancer Institute, National Institutes of Health, Bethesda, MD, USABiomedical Engineering Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USASegmentation 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.http://dx.doi.org/10.1155/2018/4185279 |
| spellingShingle | Qikui Zhu Bo Du Baris Turkbey Peter Choyke Pingkun Yan Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect Complexity |
| title | Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect |
| title_full | Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect |
| title_fullStr | Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect |
| title_full_unstemmed | Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect |
| title_short | Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect |
| title_sort | exploiting interslice correlation for mri prostate image segmentation from recursive neural networks aspect |
| url | http://dx.doi.org/10.1155/2018/4185279 |
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