Bcakground：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). While artificial qualitative analysis of meibography images could 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 MGD stages, multi-parametric analysis offering more comprehensive information could help in discovering subtle changes of meibomian glands during MGD progression. Therefore, automated and multi-parametric objective analysis of meibography images is highly demanded. Methods:The algorithm is developed to perform multi-parametric 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: (1) segmentation of the tarsal conjunctiva area as the region of interest (ROI); (2) segmentation and identification of glands within the ROI; and (3) quantitative multi-parametric analysis including newly defined gland diameter deformation index (DI), gland tortuosity index (TI), and glands signal index (SI). To evaluate the performance of the automated algorithm, the similarity index (𝑘) and the segmentation error including the false positive rate (𝑟 𝑃 ) and the false negative rate (𝑟 𝑁 ) are calculated between the manually defined ground truth and the automatic segmentations of both the ROI and meibomian glands of 15 typical meibography images. The feasibility of the algorithm is demonstrated in analyzing typical meibograhy images. Results:The results of the performance evaluation between the manually defined ground truth and the automatic segmentations are as following: 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 glands 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 has been successfully applied to process typical meibography images acquired from subjects at different meibomian gland healthy status, providing the glands area ratio GA, the gland length 𝐿, gland width 𝐷, gland diameter deformation index 𝐷𝐼, gland tortuosity index 𝑇𝐼 and glands signal index SI.Conclusions: A fully automated algorithm has been developed showing 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 objective evaluation of meibography image.

Background: Vogt-Koyanagi-Harada (VKH) disease is a multisystem autoimmune disorder which could induce bilateral panuveitis involving the posterior pole and peripheral fundus. Optical coherence tomography angiography (OCTA) provides several advantages over traditional fluorescence angiography for revealing pathological abnormalities of the retinal vasculature. Until recently, however, the OCTA field of view (FOV) was limited to 6 × 6 mm2 scans.Purpose: This study examined retinal vasculature and choriocapillaris abnormalities across multiple regions of the retina (15 × 9 mm2 wide field, macular, peripapillary regions) among acute and convalescent VKH patients using a novel widefield swept-source OCTA (WSS-OCTA) device and assessed correlations between imaging features and best-corrected visual acuity (BCVA).Methods: Twenty eyes of 13 VHK disease patients in the acute phase, 30 eyes of 17 patients in the convalescent phase, and 30 eyes of 15 healthy controls (HCs) were included in this study. Vascular length density (VLD) in superficial and deep vascular plexuses (SVP, DVP), vascular perfusion density (VPD) in SVP, DVP, and choriocapillaris (CC), and flow voids (FV) in CC were measured across multiple retinal regions via WSS-OCTA (PLEX Elite 9000, Carl Zeiss Meditec Inc., USA) using the 15 × 9 mm2 scan pattern centered on the fovea and quantified by ImageJ.Results: Compared to HCs, acute phase VKH patients exhibited significantly reduced SVP-VLD, SVP-VPD, and CC-VPD across multiple retinal regions (all p < 0.01). Notably, the FV area was more extensive in VKH patients, especially those in the acute phase (p < 0.01). These changes were reversed in the convalescent phase. Stepwise multiple linear regression analysis demonstrated that macular DVP-VLD and macular CC-VPD were the best predictive factors for BCVA in the acute and convalescent VKH groups.Conclusion: The wider field of SS-OCAT provides more comprehensive and detailed images of the microvasculature abnormalities characterizing VKH disease. The quantifiable and layer-specific information from OCTA allows for the identification of sensitive and specific imaging markers for prognosis and treatment guidance, highlighting WSS-OCTA as a promising modality for the clinical management of VKH disease.

Vascular tortuosity as an indicator of retinal vascular morphological changes can be quantitatively analyzed and used as a biomarker for the early diagnosis of relevant disease such as diabetes. While various methods have been proposed to evaluate retinal vascular tortuosity, the main obstacle limiting their clinical application is the poor consistency compared with the experts’ evaluation. In this research, we proposed to apply a multiple subdivision-based algorithm for the vessel segment vascular tortuosity analysis combining with a learning curve function of vessel curvature inflection point number, emphasizing the human assessment nature focusing not only global but also on local vascular features. Our algorithm achieved high correlation coefficients of 0.931 for arteries and 0.925 for veins compared with clinical grading of extracted retinal vessels. For the prognostic performance against experts’ prediction in retinal fundus images from diabetic patients, the area under the receiver operating characteristic curve reached 0.968, indicating a good consistency with experts’ predication in full retinal vascular network evaluation.

Objective: To compare the vessel geometry characteristics of color fundus photographs in normal control and diabetes mellitus (DM) patients and to find potential biomarkers for early diabetic retinopathy (DR) based on a neural network vessel segmentation system and automated vascular geometry parameter analysis software. Methods: A total of 102 consecutive patients with type 2 DM (T2DM) and 132 healthy controls were recruited. All participants underwent general ophthalmic examinations, and retinal fundus photographs were taken with a digital fundus camera without mydriasis. Color fundus photographs were input into a dense-block generative adversarial network (D-GAN)-assisted retinal vascular segmentation system ( http://www.gdcerc.cn:8081/#/login ) to obtain binary images. These images were then analyzed by customized software (ocular microvascular analysis system V2.9.1) for automatic processing of vessel geometry parameters, including the monofractal dimension ( Dbox), multifractal dimension ( D0), vessel area ratio ( R), max vessel diameter ( dmax), average vessel diameter ( dave), arc–chord ratio (A/C), and tortuosity (τn). Geometric differences between the healthy subjects and DM patients were analyzed. Then, regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the diagnostic efficiency of the vascular geometry parameters. Results: No significant differences were observed between the baseline characteristics of each group. DM patients had lower Dbox and D0 values (1.330 ± 0.041; 1.347 ± 0.038) than healthy subjects (1.343 ± 0.048, p < 0.05; 1.362 ± 0.042, p < 0.05) and showed increasing values of dmax, dave, A/C, and τn compared with normal controls, although only the differences in dave and τn between the groups were statistically significant. In the regression analysis, dave and τn showed a good correlation with diabetes ( dave, OR 1.765, 95% CI 1.319–2.362, p < 0.001; τn, OR 9.323, 95% CI 1.492–58.262, p < 0.05). Conclusions: We demonstrated the relationship between retinal vascular geometry and the process in DM patients, showing that Dbox, D0, dave, and τn may be indicators of morphological changes in retinal vessels in DM patients and can be early biomarkers of DR.

No abstract