Infrared Imaging Meibomian Gland Segmentation System Based on Deep Learning / 中国医疗器械杂志
Chinese Journal of Medical Instrumentation
;
(6): 377-381, 2022.
Artículo
en Chino
| WPRIM
| ID: wpr-939751
ABSTRACT
In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Diagnóstico por Imagen
/
Síndromes de Ojo Seco
/
Aprendizaje Profundo
/
Glándulas Tarsales
Tipo de estudio:
Estudio diagnóstico
Límite:
Humanos
Idioma:
Chino
Revista:
Chinese Journal of Medical Instrumentation
Año:
2022
Tipo del documento:
Artículo
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