An automated and multiparametric algorithm for objective analysis of meibography images
An automated and multiparametric algorithm for objective analysis of meibography images
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用于客观分析睑板图像的自动化多参数算法
DOI：
10.21037/qims-20-611
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发表时间：
2021-04-01
影响因子：
2.8
通讯作者：
Yuan, Jin
中科院分区：
文献类型：
--
作者：
Xiao, Peng;Luo, Zhongzhou;Yuan, Jin
背景资料：睑板造影术是一种非接触式成像技术，由眼科医生和眼科护理从业人员使用，以获取有关睑板腺特征的信息。其最重要的应用之一是协助评估和诊断睑板腺功能障碍（MGD）。由于人工定性分析睑板造影图像的可重复性和效率较低，自动化和定量评价将大大有利于图像分析过程。此外，由于睑板腺的形态和功能在MGD的不同阶段不同，多参数分析提供更全面的信息，可以帮助发现微妙的变化，腺体在MGD的进展。因此，一个自动化和多参数的客观分析的睑板造影images.Methods：一种算法被开发来执行多参数分析的睑板造影图像与全自动和可重复的分割图像对比度增强和降噪的基础上。完整的架构可以分为三个步骤：（I）将睑板结膜区域分割为感兴趣区域（ROI）;（II）分割和识别ROI内的腺体;以及（III）定量多参数分析，包括新定义的腺体直径变形指数（DI）、腺体弯曲指数（TI）和腺体信号指数（SI）。为了评估该自动化算法的性能，计算了15幅典型睑板造影图像的ROI和睑板腺的手动定义的地面真值与自动分割之间的相似性指数（k）和分割误差，包括假阳性率（r（P））和假阴性率（r（N））。手动定义的地面实况和自动分割之间的性能评估结果如下：ROI分割的相似性指数（k）=0.94 ± 0.02，假阳性率（r（P））=6.02% ± 2.41%，假阴性率（r（N））=6.43% ± 1.98%;睑板腺分割的相似性指数（k）=0.87 ± 0.01，假阳性率（r（P））=4.35% ± 1.50%，假阴性率（r（N））=18.61% ± 1.54%。该算法成功地应用于处理从不同睑板腺健康状态的受试者获得的典型睑板造影图像，通过提供腺体面积比（GA）、腺体长度（L）、腺体宽度（D）、腺体直径变形指数（DI）、腺体扭曲指数（TI）和腺体信号指数（SI）。开发了一种完全自动化的算法，与手动方法相比，该算法表现出对于睑板造影图像分割的高度相似性和适度的分割误差，提供多个参数来量化睑板腺的形态和功能，用于睑板造影图像的客观评价。
Background: Meibography is a non-contact imaging technique used by ophthalmologists and eye care practitioners to acquire information on the characteristics of meibomian glands. One of its most important applications is to assist in the evaluation and diagnosis of meibomian gland dysfunction (MGD). As the artificial qualitative analysis of meibography images can lead to low repeatability and efficiency, automated and quantitative evaluation would greatly benefit the image analysis process. Moreover, since the morphology and function of meibomian glands varies at different stages of MGD, multiparametric analysis offering more comprehensive information could help in discovering subtle changes to glands during MGD progression. Therefore, an automated and multiparametric objective analysis of meibography images is urgently needed.Methods: An algorithm was developed to perform multiparametric analysis of meibography images with fully automatic and repeatable segmentation based on image contrast enhancement and noise reduction. The full architecture can be divided into three steps: (I) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (II) segmentation and identification of glands within the ROI; and (III) quantitative multiparametric analysis including a newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI). To evaluate the performance of this automated algorithm, the similarity index (k) and the segmentation error including the false-positive rate (r(P)) and the false-negative rate (r(N)) were calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images.Results: The results of the performance evaluation between the manually defined ground truth and automatic segmentations were as follows: for ROI segmentation, the similarity index (k)=0.94 +/- 0.02, the false-positive rate (r(P))=6.02%+/- 2.41%, and the false-negative rate (r(N))=6.43%+/- 1.98%; for meibomian gland segmentation, the similarity index (k)=0.87 +/- 0.01, the false-positive rate (r(P))=4.35%+/- 1.50%, and the-false negative rate (r(N))=18.61%+/- 1.54%. The algorithm was successfully applied to process typical meibography images acquired from subjects of different meibomian gland health statuses, by providing the gland area ratio (GA), the gland length (L), gland width (D), gland diameter deformation index (DI), gland tortuosity index (TI), and gland signal index (SI).Conclusions: A fully automated algorithm was developed which demonstrated high similarity with moderate segmentation errors for meibography image segmentation compared with the manual approach, offering multiple parameters to quantify the morphology and function of meibomian glands for the objective evaluation of meibography images.