Detection of defected nerve regions on retinal fundus images using OCT data for glaucoma screening

Chisako Muramatsu; Ryusuke Watanabe; Akira Sawada; Yuji Hatanaka; Takeshi Hara; Tetsuya Yamamoto; Hiroshi Fujita

doi:10.1117/12.2549920

2 March 2020 Detection of defected nerve regions on retinal fundus images using OCT data for glaucoma screening

Chisako Muramatsu, Ryusuke Watanabe, Akira Sawada, Yuji Hatanaka, Takeshi Hara, Tetsuya Yamamoto, Hiroshi Fujita

Author Affiliations +

Proceedings Volume 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications; 1131819 (2020) https://doi.org/10.1117/12.2549920
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. When the retinal nerve is damaged, the thickness of the nerve fiber layer decreases. It is difficult, however, to detect subtle change in early disease stages on retinal fundus photographs. Although an optical coherence tomography (OCT) is generally more sensitive and can evaluate the thicknesses of retinal layers, it is performed as a diagnostic exam rather than screening exam. Retinal fundus photographs are frequently performed for diagnosis and follow-ups at ophthalmology visits and for general health checkups. It will be useful if suspicious regions can be detected on retinal photographs. The purpose of this study is to estimate the regions of defected nerves on retinal photographs using the deep learning model trained by OCT data. The network is based on the fully convolutional network. The region including an optic disc is extracted from the retinal photographs and is used as the input data. The OCT image of the same patient is registrated to the retinal image based on the blood vessel networks, and the deviation map specifying the regions with decreased nerve layer thickness is used as teacher data. The proposed method achieved 76% accuracy in assessing the defected and non-defected regions. It can be useful as a screening tool and for visual assistance in glaucoma diagnosis.

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Chisako Muramatsu, Ryusuke Watanabe, Akira Sawada, Yuji Hatanaka, Takeshi Hara, Tetsuya Yamamoto, and Hiroshi Fujita "Detection of defected nerve regions on retinal fundus images using OCT data for glaucoma screening", Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131819 (2 March 2020); https://doi.org/10.1117/12.2549920

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KEYWORDS

Optical coherence tomography

Photography

Image segmentation

Defect detection

Image registration

Visualization

Blood vessels

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