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: To explore the performance of quantitative morphological and functional analysis in meibography images by an automatic meibomian glands (MGs) analyser in diagnosis and grading Meibomian Gland Dysfunction (MGD). Methods: A cross-sectional study collected 256 subjects with symptoms related to dry eye and 56 healthy volunteers who underwent complete ocular surface examination was conducted between January 1, 2019, and December 31, 2020. The 256 symptomatic subjects were classified into MGD group (n = 195) and symptomatic non-MGD group (n = 61). An automatic MGs analyser was used to obtained multi-parametric measurements in meibography images including the MGs area ratio (GA), MGs diameter deformation index (DI), MGs tortuosity index (TI), and MGs signal index (SI). Adjusted odds ratios (ORs) of the multi-parametric measurements of MGs for MGD, and the area under the receiver operating characteristic (AUC-ROC) curves of multiparametric measurements for MGD diagnosing and grading were conducted. Findings: When consider age, sex, ocular surface condition together, the estimated ORs for DI was 1.62 (95% CI, 1.29-2.56), low-level SI was 24.34 (95% CI, 2.73-217.3), TI was 0.76(95% CI, 0.54-0.90), and GA was 0.86 (95% CI, 0.74-0.92) for MGD. The combination of DI-TI-GA-SI showed an AUC = 0.82 (P < 0.001) for discriminating MGD from symptomatic subjects. The DI had a higher AUC in identifying early-stage MGD (grade 1-2), while TI and GA had higher AUCs in moderate and advanced stages (grade 3-5). Merging DI-TI-GA showed the highest AUCs in distinguish MGD severities. Interpretation: The MGs area ratio, diameter deformation, tortuosity and signal intensity could be considered promising biomarkers for MGD diagnosis and objective grading.
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.
To expand the clinical applications and improve the ease of use of ultrahigh-resolution optical coherence tomography (UHR-OCT), we developed a portable boom-type ophthalmic UHR-OCT operating in supine position that can be used for pediatric subjects, bedridden patients and perioperative conditions. By integrating the OCT sample arm probe with real-time iris display and automatic focusing electric lens for easy alignment, coupling the probe on a self-locking multi-directional manipulator to reduce motion artifacts and operator fatigue, and installing the OCT module on a moveable cart for system mobility, our customized portable boom-type UHR-OCT enables non-contact, high-resolution and high-stability retinal examinations to be performed on subjects in supine position. The spectral-domain UHR-OCT operates at a wavelength of 845 nm with 130 nm FWHM (full width at half maximum) bandwidth, achieving an axial resolution of ≈2.3µm in tissue with an A-line acquisition rate up to 128 kHz. A high-definition two-dimensional (2D) raster protocol was used for high-quality cross-sectional imaging while a cube volume three-dimensional (3D) scan was used for three-dimensional imaging and en-face reconstruction, resolving major layer structures of the retina. The feasibility of the system was demonstrated by performing supine position 2D/3D retinal imaging on healthy human subjects, sedated infants, and non-sedated awake neonates.
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