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http://dx.doi.org/10.9717/kmms.2020.23.12.1476

Segmenting Layers of Retinal OCT Images using cGAN  

Kwon, Oh-Heum (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Song, Ha-Joo (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.12, 2020 , pp. 1476-1485 More about this Journal
Abstract
Segmenting OCT retinal images into layers is important to diagnose and understand the progression of retinal diseases or identify potential symptoms. The task of manually identifying these layers is a difficult task that requires a lot of time and effort even for medical professionals, and therefore, various studies are being conducted to automate this using deep learning technologies. In this paper, we use cGAN-based neural network to automatically segmenting OCT retinal images into seven terrain-type regions defined by six layer boundaries. The network is composed of a Segnet-based generator model and a discriminator model. We also proposed a dynamic programming algorithm for refining the outputs of the network. We performed experiments using public OCT image data set and compared its performance with the Segnet-only version of the network. The experimental results show that the cGAN-based network outperforms Segnet-only version.
Keywords
Conditional GAN; Layer Segmentation; Optical Coherence Tomography; Deep Learning;
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