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http://dx.doi.org/10.9717/kmms.2019.22.11.1269

Layer Segmentation of Retinal OCT Images using Deep Convolutional Encoder-Decoder Network  

Kwon, Oh-Heum (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Song, Min-Gyu (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Song, Ha-Joo (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.11, 2019 , pp. 1269-1279 More about this Journal
Abstract
In medical image analysis, segmentation is considered as a vital process since it partitions an image into coherent parts and extracts interesting objects from the image. In this paper, we consider automatic segmentations of OCT retinal images to find six layer boundaries using convolutional neural networks. Segmenting retinal images by layer boundaries is very important in diagnosing and predicting progress of eye diseases including diabetic retinopathy, glaucoma, and AMD (age-related macular degeneration). We applied well-known CNN architecture for general image segmentation, called Segnet, U-net, and CNN-S into this problem. We also proposed a shortest path-based algorithm for finding the layer boundaries from the outputs of Segnet and U-net. We analysed their performance on public OCT image data set. The experimental results show that the Segnet combined with the proposed shortest path-based boundary finding algorithm outperforms other two networks.
Keywords
Optical Coherence Tomography; Image Segmentation; Convolutional Neural Network; Deep Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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