Choroid segmentation from Optical Coherence Tomography with graph-edge weights learned from deep convolutional neural networks
Examining choroid in Optical Coherence Tomography (OCT) plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing approaches to detecting choroidal boundaries, graph-searching based techniques belong to the state-of-the-art. However, most of these techniques rely o...
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| Published in | Neurocomputing (Amsterdam) Vol. 237; pp. 332 - 341 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
10.05.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2017.01.023 |
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| Abstract | Examining choroid in Optical Coherence Tomography (OCT) plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing approaches to detecting choroidal boundaries, graph-searching based techniques belong to the state-of-the-art. However, most of these techniques rely on hand-crafted models on the graph-edge weight and their performances are limited mainly due to the weak choroidal boundaries, textural structure of the choroid, inhomogeneity of the textural structure of the choroid and great variation of the choroidal thickness. In order to circumvent this limitation, we present a multi-scale and end-to-end convolutional network architecture where an optimal graph-edge weight can be learned directly from raw pixels. Our method operates on multiple scales and combines local and global information from the 2D OCT image. Experimental results obtained based on 912 OCT B-scans show that our learned graph-edge weights outperform conventional hand-crafted ones and behave robustly and accurately no matter the OCT image is from normal subjects or patients for whom significant retinal structure variations can be observed. |
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| AbstractList | Examining choroid in Optical Coherence Tomography (OCT) plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing approaches to detecting choroidal boundaries, graph-searching based techniques belong to the state-of-the-art. However, most of these techniques rely on hand-crafted models on the graph-edge weight and their performances are limited mainly due to the weak choroidal boundaries, textural structure of the choroid, inhomogeneity of the textural structure of the choroid and great variation of the choroidal thickness. In order to circumvent this limitation, we present a multi-scale and end-to-end convolutional network architecture where an optimal graph-edge weight can be learned directly from raw pixels. Our method operates on multiple scales and combines local and global information from the 2D OCT image. Experimental results obtained based on 912 OCT B-scans show that our learned graph-edge weights outperform conventional hand-crafted ones and behave robustly and accurately no matter the OCT image is from normal subjects or patients for whom significant retinal structure variations can be observed. |
| Author | Yin, Yilong Pan, Xuemei Sui, Xiaodan Zheng, Yuanjie Bi, Hongsheng Zhang, Shaoting Wei, Benzheng Wu, Jianfeng |
| Author_xml | – sequence: 1 givenname: Xiaodan surname: Sui fullname: Sui, Xiaodan organization: School of Information Science and Engineering, Shandong Normal University, Shandong, China – sequence: 2 givenname: Yuanjie surname: Zheng fullname: Zheng, Yuanjie email: yjzheng@sdnu.edu.cn organization: School of Information Science and Engineering, Shandong Normal University, Shandong, China – sequence: 3 givenname: Benzheng surname: Wei fullname: Wei, Benzheng organization: Shandong University of Traditional Chinese Medicine, Shandong, China – sequence: 4 givenname: Hongsheng surname: Bi fullname: Bi, Hongsheng organization: Eye Institute of Shandong University of TCM, Shandong, China – sequence: 5 givenname: Jianfeng surname: Wu fullname: Wu, Jianfeng organization: Shandong University of Traditional Chinese Medicine, Shandong, China – sequence: 6 givenname: Xuemei surname: Pan fullname: Pan, Xuemei organization: Affiliated Eye Hospital of Shandong University of TCM, Shandong, China – sequence: 7 givenname: Yilong orcidid: 0000-0002-5786-2491 surname: Yin fullname: Yin, Yilong organization: School of Computer Science and Technology, Shandong University, Shandong, China – sequence: 8 givenname: Shaoting surname: Zhang fullname: Zhang, Shaoting organization: Department of Computer Science, University of North Carolina at Charlotte, NC, USA |
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