Optic cup and disc segmentation model based on linear attention and dual attention.
10.7507/1001-5515.202208061
- Author:
Zijun LAN
1
;
Jun XIE
1
;
Yan GUO
2
;
Zhe ZHANG
3
;
Bin SUN
1
Author Information
1. College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi 030600, P. R. China.
2. Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing 100029, P. R. China.
3. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, P. R. China.
- Publication Type:Journal Article
- Keywords:
Dual attention;
Glaucoma;
Knowledge distillation;
Linear attention;
Optic cup and disc segmentation
- MeSH:
Humans;
Optic Disk/diagnostic imaging*;
Glaucoma/diagnosis*;
Algorithms;
Diagnostic Techniques, Ophthalmological;
Databases, Factual
- From:
Journal of Biomedical Engineering
2023;40(5):920-927
- CountryChina
- Language:Chinese
-
Abstract:
Glaucoma is one of blind causing diseases. The cup-to-disc ratio is the main basis for glaucoma screening. Therefore, it is of great significance to precisely segment the optic cup and disc. In this article, an optic cup and disc segmentation model based on the linear attention and dual attention is proposed. Firstly, the region of interest is located and cropped according to the characteristics of the optic disc. Secondly, linear attention residual network-34 (ResNet-34) is introduced as a feature extraction network. Finally, channel and spatial dual attention weights are generated by the linear attention output features, which are used to calibrate feature map in the decoder to obtain the optic cup and disc segmentation image. Experimental results show that the intersection over union of the optic disc and cup in Retinal Image Dataset for Optic Nerve Head Segmentation (DRISHTI-GS) dataset are 0.962 3 and 0.856 4, respectively, and the intersection over union of the optic disc and cup in retinal image database for optic nerve evaluation (RIM-ONE-V3) are 0.956 3 and 0.784 4, respectively. The proposed model is better than the comparison algorithm and has certain medical value in the early screening of glaucoma. In addition, this article uses knowledge distillation technology to generate two smaller models, which is beneficial to apply the models to embedded device.
