Segmentation of meibomian glands based on deep learning
10.3980/j.issn.1672-5123.2022.7.25
- VernacularTitle:基于深度学习的睑板腺腺体分割方法研究
- Author:
Jia-Wen LIN
1
,
2
;
Zhi-Ming LIN
1
,
2
;
Tai-Chen LAI
1
,
2
;
Lin-Ling GUO
1
,
2
;
Jing ZOU
1
,
2
;
Li LI
1
,
2
Author Information
1. College of Computer and Data Science, Fuzhou University, Fuzhou 350108, Fujian Province, China
2. Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350108, Fujian Province, China
- Publication Type:Journal Article
- Keywords:
meibomian gland dysfunction;
infrared meibomian gland images;
gland segmentation;
deep learning;
UNet++
- From:
International Eye Science
2022;22(7):1191-1194
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To explore the application value of deep learning technology in automatic meibomian glands segmentation. METHODS:Infrared meibomian gland images were collected and 193 of them were picked out for establishing the database. The images were manually labeled by three clinicians. UNet++ network and automatic data expansion strategy were introduced to construct the automatic meibomian glands segmentation model. The feasibility and effectiveness of the proposed segmentation model were analyzed by precision, sensitivity, specificity, accuracy and intersection over union.RESULTS: Taking manual labeling as the gold standard, the presented method segment the glands effectively and steadily with accuracy, sensitivity and specificity of 94.31%, 82.15% and 96.13% respectively. On the average, only 0.11s was taken for glands segmentation of single image.CONCLUSIONS: In this paper, deep learning technology is introduced to realize automatic segmentation of meibomian glands, achieving high accuracy, good stability and efficiency. It would be quite useful for calculation of gland morphological parameters, the clinical diagnosis and screening of related diseases, improving the diagnostic efficiency.
