该研究利用全卷积网络（FC-DenseNet）对彩色眼底照片进行视盘（OD）和杯（OC）的分割，以辅助青光眼诊断。在ORIGA、DRIONS-DB、Drishti-GS、ONHSD和RIM-ONE数据集上进行了实验，展示了深度学习方法在不同数据集上的泛化能力。尽管训练时间较长，但在测试时速度快，精度高，特别是在部分数据集上表现出最佳性能。
23
2018 symmetry
Method ： 分割
Dataset： colour retinal fundus images
train ：ORIGA
test ：ORIGA , DRIONS-DB , Drishti-GS , ONHSD , and RIM-ONE
Architecture： crop ROI + FC-DenseNet （ FCN + DenseNet）+ Refinement
Results: 见后续表格
分割 OD/OC 的introduce 写的比较详细 （shape-based template - matching；active contours deformable based models ； machine and deep learning methods ）
Method
Three Method of automated OD and OC segmentation in fundus images
-
shape-based template matching [3,4,5,6,7,8,9]
- Method ：
model the OD as a circular or elliptical object and try to fit a circle using the Hough transform [4,5,8,9], an ellipse [3,6] or a rounded curve using a sliding band filter [7].
These approaches typically feature in the earlier work
- Deficiency ： not robust enough
In general, these shape-based modelling approaches to OD and OC segmentation are not robust enough due to intensity inhomogeneity, varying image colour, changes in disc shape by lesions such as exudates present in abnormal images, and the presence of blood vessels inside and around the OD
- Method ：
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