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Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior

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Abstract

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.

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Acknowledgements

This work is supported by Natural Science Foundation of China (61701280), Natural Science Foundation of Shandong Province (ZR2016FQ18, ZR2017QF009), National Basic Research Program of China (973 Program) under Grant 2014CB748600, National Science Fund for Outstanding Young Scholars (61622114), Natural Science Foundation of China (61573219, 81371629, 61671274, 61703235, 61701281), the Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions, Shandong Provincial Key Research and Development Plan (Grant no. 2017CXGC1504). The Fostering Project of Dominant Displine and Talent Team of SDUFE. The authors would like to greatly thank the editors and the reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University of Finance and Economics, **an, China

    ** Yang & Yilong Yin

  2. Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, China

    Haoyu Chen

  3. Taian Institute of Science and Technology Information, Taian, China

    **n Fan

  4. School of Electronic and Information Engineering, Soochow University, Suzhou, China

    **njian Chen

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  1. **aoming **
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  2. **an**g Meng
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Correspondence to Yilong Yin or **njian Chen.

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**, X., Meng, X., Yang, L. et al. Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior. Multimedia Systems 25, 95–102 (2019). https://doi.org/10.1007/s00530-017-0582-5

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