Joint optic disc and cup segmentation based on residual multi-scale fully convolutional neural network.
10.7507/1001-5515.201909006
- Author:
Xin YUAN
1
;
Xiujuan ZHENG
1
;
Bin JI
2
,
3
;
Miao LI
1
;
Bin LI
1
Author Information
1. Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, P.R.China.
2. Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, P.R.China
3. China Mobile (Chengdu) Industrial Research Institute, Chengdu 610041, P.R.China.
- Publication Type:Journal Article
- Keywords:
deep learning;
fully convolutional neural network;
glaucoma screening;
optic cup segmentation;
optic disc segmentation
- MeSH:
Diagnostic Techniques, Ophthalmological;
Fundus Oculi;
Glaucoma/diagnostic imaging*;
Humans;
Neural Networks, Computer;
Optic Disk/diagnostic imaging*
- From:
Journal of Biomedical Engineering
2020;37(5):875-884
- CountryChina
- Language:Chinese
-
Abstract:
Glaucoma is the leading cause of irreversible blindness, but its early symptoms are not obvious and are easily overlooked, so early screening for glaucoma is particularly important. The cup to disc ratio is an important indicator for clinical glaucoma screening, and accurate segmentation of the optic cup and disc is the key to calculating the cup to disc ratio. In this paper, a full convolutional neural network with residual multi-scale convolution module was proposed for the optic cup and disc segmentation. First, the fundus image was contrast enhanced and polar transformation was introduced. Subsequently, W-Net was used as the backbone network, which replaced the standard convolution unit with the residual multi-scale full convolution module, the input port was added to the image pyramid to construct the multi-scale input, and the side output layer was used as the early classifier to generate the local prediction output. Finally, a new multi-tag loss function was proposed to guide network segmentation. The mean intersection over union of the optic cup and disc segmentation in the REFUGE dataset was 0.904 0 and 0.955 3 respectively, and the overlapping error was 0.178 0 and 0.066 5 respectively. The results show that this method not only realizes the joint segmentation of cup and disc, but also improves the segmentation accuracy effectively, which could be helpful for the promotion of large-scale early glaucoma screening.
