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15 March 2019 Automated segmentation of the optic disc using the deep learning
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Abstract
Accurate segmentation of the optic disc (OD) depicted on color fundus images plays an important role in the early detection and quantitative diagnosis of retinal diseases, such as glaucoma and optic atrophy. In this study, we proposed a coarse-to-fine deep learning framework on the basis of a classical convolutional neural network (CNN), known as the Unet model, for extracting the optic disc from fundus images. This network was trained separately on fundus images and their vessel density maps, leading to two coarse segmentation results from the entire images. We combined the results using an overlap strategy to identify a local image patch (disc candidate region), which was then fed into the U-net model for further segmentation. Our experiments demonstrated that the developed framework achieved an average intersection over union (IoU) and a dice similarity coefficient (DSC) of 89.1% and 93.9%, respectively, based on a total of 2,978 test images from our collected dataset and six public datasets, as compared to 87.4% and 92.5% obtained by only using the sole U-net model. This suggests that the proposed method can provide better segmentation performances and have the potential for population based disease screening.
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Lei Wang, Han Liu, Jian Zhang, Hang Chen, and Jiantao Pu "Automated segmentation of the optic disc using the deep learning", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094923 (15 March 2019); https://doi.org/10.1117/12.2510372
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