标题:Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior
作者:Xi, Xiaoming; Meng, Xianjing; Yang, Lu; Nie, Xiushan; Yang, Gongping; Chen, Haoyu; Fan, Xin; Yin, Yilong; Chen, Xinjian
通讯作者:Yin, Yilong;Yin, YL;Chen, XJ
作者机构:[Xi, Xiaoming; Meng, Xianjing; Yang, Lu; Nie, Xiushan] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.; [Yan 更多
来源:MULTIMEDIA SYSTEMS
出版年:2019
卷:25
期:2
页码:95-102
DOI:10.1007/s00530-017-0582-5
关键词:Choroidal neovascularization (CNV); Optical coherence tomography (OCT);; Segmentation; Structure prior; Convolutional neural networks (CNN)
摘要:Automated segmentation of choroidal neovascularization (CNV) in optical coherence tomography (OCT) images plays an important role for the treatment of CNV disease. This paper proposes multi-scale convolutional neural networks with structure prior to segment CNV from OCT data. The proposed framework consists of two stages. In the first stage, the structure prior learning method based on sparse representation-based classification and the local potential function is developed to capture the global spatial structure and local similarity structure prior. The obtained prior can be used to improve the distinctiveness between CNV and background patches. In the second stage, multi-scale CNN model with incorporation of the learned structure prior is constructed for CNV segmentation. In this stage, multi-scale analysis is used to capture effective contextual information, which is robust to varying sizes of CNV. The proposed method was evaluated on 15 spectral domain OCT data with CNV. The experimental results demonstrate the effectiveness of proposed method.
收录类别:EI;SCIE
资源类型:会议论文;期刊论文
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