期刊
JOURNAL OF COMPUTATIONAL SCIENCE
卷 20, 期 -, 页码 70-79出版社
ELSEVIER
DOI: 10.1016/j.jocs.2017.02.006
关键词
Optic disc segmentation; Blood vessels segmentation; Fovea segmentation; Convolutional neural network; Fundus image
资金
- National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust
- UCL Institute of Ophthalmology
We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels. Fundus images were normalized before segmentation was performed to enforce consistency in background lighting and contrast. For every effective point in the fundus image, our algorithm extracted three channels of input from the point's neighbourhood and forwarded the response across the 7-layer network. The output layer consists of four neurons, representing background, optic disc, fovea and blood vessels. In average, our segmentation correctly classified 92.68% of the ground truths (on the testing set from Drive database). The highest accuracy achieved on a single image was 94.54%, the lowest 88.85%. A single convolutional neural network can be used not just to segment blood vessels, but also optic disc and fovea with good accuracy. (C) 2017 Elsevier B.V. All rights reserved.
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