人工智能
计算机科学
分割
视盘
深度学习
卷积神经网络
眼底(子宫)
模式识别(心理学)
Sørensen–骰子系数
图像分割
相似性(几何)
交叉口(航空)
视杯(胚胎学)
计算机视觉
青光眼
图像(数学)
眼科
地图学
表型
生物化学
眼睛发育
化学
地理
基因
医学
作者
Lei Wang,Han Liu,Yue Lu,Hang Chen,Jian Zhang,Jiantao Pu
标识
DOI:10.1016/j.bspc.2019.01.022
摘要
Accurate segmentation of the optic disc (OD) depicted on color fundus images may aid 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 U-net model, to accurately identify the optic disc. This network was trained separately on color fundus images and their grayscale vessel density maps, leading to two different segmentation results from the entire image. 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 2978 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. The comparison with available approaches demonstrated a reliable and relatively high performance of the proposed deep learning framework in automated OD segmentation.
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