Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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AMD Identification from OCT Volume Data using Deep Convolutional Neural Network

심층 컨볼루션 신경망을 이용한 OCT 볼륨 데이터로부터 AMD 진단

  • Kwon, Oh-Heum (Dept. of IT Convergence and Application, Pukyong National University) ;
  • Jung, Yoo Jin (Division of Computer & Electronic Systems Engineering, Hankuk University of Foreign Studies) ;
  • Song, Ha-Joo (Dept. of IT Convergence and Application, Pukyong National University)
  • Received : 2017.07.17
  • Accepted : 2017.08.01
  • Published : 2017.08.31
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Abstract

Optical coherence tomography (OCT) is the most common medical imaging device with the ability to image the retina in eyes at micrometer resolution and to visualize the pathological indicators of many retinal diseases such as Age-Related Macular Degeneration (AMD) and diabetic retinopathy. Accordingly, there have been research activities to analyze and process OCT images to identify those indicators and make medical decisions based on the findings. In this paper, we use a deep convolutional neural network for analysis of OCT volume data to distinguish AMD from normal patients. We propose a novel approach where images in each OCT volume are grouped together into sub-volumes and the network is trained by those sub-volumes instead of individual images. We conducted an experiment using public data set to evaluate the performance of the proposed approach. The experiment confirmed performance improvement of our approach over the traditional image-by-image training approach.

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References

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