Automated measurement of the disc-fovea angle based on DeepLabv3.Purpose
 To assess the value of automatic disc-fovea angle (DFA) measurement using the DeepLabv3+ segmentation model.


 A total of 477 normal fundus images were used to train DeepLabv3+, U-Net, and PSPNet model, which were used to obtain the optic disc area and virtual macular area.  The experimental results showed that the errors in automatic DFA measurement using DeepLabv3+, U-Net, and PSPNet segmentation models were 0.76°, 1.4°, and 2.12°, respectively.  The mean intersection over union (MIoU), mean pixel accuracy (MPA), average error in the center of the optic disc, and average error in the center of the virtual macula obstained by using DeepLabv3+ model was 94.77%, 97.32%, 10.94 pixels, and 13.44 pixels, respectively.  The automatic DFA measurement using DeepLabv3+ got the less error than the errors that using the other segmentation models.  Therefore, the DeepLabv3+ segmentation model was finally chosen to measure DFA automatically.


 The DeepLabv3+ segmentation model -based automatic segmentation techniques can produce accurate and rapid DFA measurements.
DeepLabv3
A total of 477 normal fundus images were used to train DeepLabv3+, U-Net, and PSPNet model, which were used to obtain the optic disc area and virtual macular area.  The experimental results showed that the errors in automatic DFA measurement using DeepLabv3+, U-Net, and PSPNet segmentation models were 0.76°, 1.4°, and 2.12°, respectively. 
U-Net
A total of 477 normal fundus images were used to train DeepLabv3+, U-Net, and PSPNet model, which were used to obtain the optic disc area and virtual macular area.  The experimental results showed that the errors in automatic DFA measurement using DeepLabv3+, U-Net, and PSPNet segmentation models were 0.76°, 1.4°, and 2.12°, respectively. 
PSPNet