International Computer Science Institute, Berkeley, California.
Clinical Research Center, School of Optometry, University of California, Berkeley, Berkeley, California.
Optom Vis Sci. 2021 Sep 1;98(9):1094-1103. doi: 10.1097/OPX.0000000000001767.
Quantifying meibomian gland morphology from meibography images is used for the diagnosis, treatment, and management of meibomian gland dysfunction in clinics. A novel and automated method is described for quantifying meibomian gland morphology from meibography images.
Meibomian gland morphological abnormality is a common clinical sign of meibomian gland dysfunction, yet there exist no automated methods that provide standard quantifications of morphological features for individual glands. This study introduces an automated artificial intelligence approach to segmenting individual meibomian gland regions in infrared meibography images and analyzing their morphological features.
A total of 1443 meibography images were collected and annotated. The dataset was then divided into development and evaluation sets. The development set was used to train and tune deep learning models for segmenting glands and identifying ghost glands from images, whereas the evaluation set was used to evaluate the performance of the model. The gland segmentations were further used to analyze individual gland features, including gland local contrast, length, width, and tortuosity.
A total of 1039 meibography images (including 486 upper and 553 lower eyelids) were used for training and tuning the deep learning model, whereas the remaining 404 images (including 203 upper and 201 lower eyelids) were used for evaluations. The algorithm on average achieved 63% mean intersection over union in segmenting glands, and 84.4% sensitivity and 71.7% specificity in identifying ghost glands. Morphological features of each gland were also fed to a support vector machine for analyzing their associations with ghost glands. Analysis of model coefficients indicated that low gland local contrast was the primary indicator for ghost glands.
The proposed approach can automatically segment individual meibomian glands in infrared meibography images, identify ghost glands, and quantitatively analyze gland morphological features.
从睑板腺照相图像中量化睑板腺形态用于临床中睑板腺功能障碍的诊断、治疗和管理。本文描述了一种从睑板腺照相图像中量化睑板腺形态的新颖自动化方法。
睑板腺形态异常是睑板腺功能障碍的常见临床征象，但目前尚缺乏对单个腺体的形态特征进行标准化量化的自动化方法。本研究引入了一种自动化人工智能方法，用于分割红外睑板腺照相图像中的单个睑板腺区域并分析其形态特征。
共采集和标注了 1443 张睑板腺图像。数据集随后分为开发集和评估集。开发集用于训练和调整用于从图像中分割腺体和识别幽灵腺体的深度学习模型，而评估集用于评估模型的性能。进一步使用腺体分割来分析单个腺体特征，包括腺体局部对比度、长度、宽度和扭曲度。
共使用 1039 张睑板腺图像（包括 486 个上睑和 553 个下睑）进行训练和调整深度学习模型，而其余 404 张图像（包括 203 个上睑和 201 个下睑）用于评估。该算法平均在分割腺体方面实现了 63%的平均交并比，在识别幽灵腺体方面实现了 84.4%的灵敏度和 71.7%的特异性。每个腺体的形态特征也被输入支持向量机进行分析，以研究它们与幽灵腺体的关系。模型系数分析表明，低腺体局部对比度是幽灵腺体的主要指标。
所提出的方法可以自动分割红外睑板腺照相图像中的单个睑板腺，识别幽灵腺体，并定量分析腺体形态特征。
