Segmentation of meibomian glands based on deep learning / 国际眼科杂志(Guoji Yanke Zazhi)
International Eye Science
; (12): 1191-1194, 2022.
Article
in Zh
| WPRIM
| ID: wpr-929505
Responsible library:
WPRO
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.
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Index:
WPRIM
Type of study:
Guideline
Language:
Zh
Journal:
International Eye Science
Year:
2022
Type:
Article