方法：采集并筛选出193幅红外睑板腺图像构建图像数据库，由3名临床医师对图像进行人工标记； 引入UNet++网络与自动数据增广策略构建睑板腺腺体自动分割模型，采用精确率、敏感性、特异性、准确率和交并比分析该模型的可行性与有效性。

结果：以人工标注结果为金标准，基于UNet++的睑板腺腺体自动分割模型取得94.31%的准确率，敏感性、特异性分别为82.15%和96.13%，腺体分割表现具有较好的稳定性，模型处理单张图像的平均用时仅为0.11s。

结论：引入深度学习技术实现睑板腺腺体的自动分割，具有良好的准确性、稳定性和高效性，可服务于睑板腺功能障碍患者腺体形态参数的计算，辅助相关疾病的临床诊断和筛查，提高诊断效率。

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.