Purpose

Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.

Methods

A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multiple revisions by investigators and graded 6 times by meibomian gland dysfunction (MGD) experts. Two deep learning (DL) models were trained separately to segment areas of the MG and eyelid. Those segmentation were used to estimate MG ratio and meiboscores using a classification-based DL model. A generative adversarial network was implemented to remove specular reflections from original images.

Results

The mean ratio of MG calculated by investigator annotation and DL segmentation was consistent 26.23% vs 25.12% in the upper eyelids and 32.34% vs. 32.29% in the lower eyelids, respectively. Our DL model achieved 73.01% accuracy for meiboscore classification on validation set and 59.17% accuracy when tested on images from independent center, compared to 53.44% validation accuracy by MGD experts. The DL-based approach successfully removes reflection from the original MG images without affecting meiboscore grading.

Conclusions

DL with infrared meibography provides a fully automated, fast quantitative evaluation of MG morphology (MG Segmentation, MG area, MG ratio, and meiboscore) which are sufficiently accurate for diagnosing dry eye disease. Also, the DL removes specular reflection from images to be used by ophthalmologists for distraction-free assessment.

中文翻译：

---

使用深度学习的红外线描记术中睑板腺脱落的自动量化

目的

MG 开发一种基于深度学习的自动化方法来分割睑板腺 (MG) 和眼睑，定量分析 MG 面积和 MG 比率，估计 meiboscore，并从红外图像中去除镜面反射。

方法

在临床环境中总共捕获了 1600 张梅博图像。研究人员对 1000 张图像进行了多次修改并进行了精确注释，并由睑板腺功能障碍 (MGD) 专家进行了 6 次评分。分别训练了两个深度学习 (DL) 模型来分割 MG 和眼睑的区域。这些分割用于使用基于分类的 DL 模型估计 MG 比率和 meiboscores。实施生成对抗网络以消除原始图像的镜面反射。

结果

通过研究者注释和 DL 分割计算的 MG 平均比率在上眼睑中分别为 26.23% 和 25.12%，在下眼睑中分别为 32.34% 和 32.29%。我们的 DL 模型在验证集上的 meiboscore 分类准确率达到 73.01%，在独立中心的图像上测试时达到 59.17% 的准确率，而 MGD 专家的验证准确率为 53.44%。基于 DL 的方法成功地去除了原始 MG 图像的反射，而不影响 meiboscore 评分。

结论

DL 与红外 meibography 提供了 MG 形态（MG 分割、MG 面积、MG 比率和 meiboscore）的全自动、快速定量评估，这对于诊断干眼病来说足够准确。此外，DL 还消除了图像中的镜面反射，供眼科医生用于进行无干扰评估。