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Objective: To image healthy retinal pigment epithelial (RPE) cells in vivo using Transscleral OPtical Imaging (TOPI) and to analyze statistics of RPE cell features as a function of age, axial length (AL), and eccentricity. Design: Single-center, exploratory, prospective, and descriptive clinical study. Participants: Forty-nine eyes (AL: 24.03 ± 0.93 mm; range: 21.9-26.7 mm) from 29 participants aged 21 to 70 years (37.1 ± 13.3 years; 19 men, 10 women). Methods: Retinal images, including fundus photography and spectral-domain OCT, AL, and refractive error measurements were collected at baseline. For each eye, 6 high-resolution RPE images were acquired using TOPI at different locations, one of them being imaged 5 times to evaluate the repeatability of the method. Follow-up ophthalmic examination was repeated 1 to 3 weeks after TOPI to assess safety. Retinal pigment epithelial images were analyzed with a custom automated software to extract cell parameters. Statistical analysis of the selected high-contrast images included calculation of coefficient of variation (CoV) for each feature at each repetition and Spearman and Mann-Whitney tests to investigate the relationship between cell features and eye and subject characteristics. Main Outcome Measures: Retinal pigment epithelial cell features: density, area, center-to-center spacing, number of neighbors, circularity, elongation, solidity, and border distance CoV. Results: Macular RPE cell features were extracted from TOPI images at an eccentricity of 1.6° to 16.3° from the fovea. For each feature, the mean CoV was < 4%. Spearman test showed correlation within RPE cell features. In the perifovea, the region in which images were selected for all participants, longer AL significantly correlated with decreased RPE cell density (R Spearman, Rs = -0.746; P < 0.0001) and increased cell area (Rs = 0.668; P < 0.0001), without morphologic changes. Aging was also significantly correlated with decreased RPE density (Rs = -0.391; P = 0.036) and increased cell area (Rs = 0.454; P = 0.013). Lower circular, less symmetric, more elongated, and larger cells were observed in those > 50 years. Conclusions: The TOPI technology imaged RPE cells in vivo with a repeatability of < 4% for the CoV and was used to analyze the influence of physiologic factors on RPE cell morphometry in the perifovea of healthy volunteers. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

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OBJECTIVE: To develop a fully automated method of retinal pigmented epithelium (RPE) cells detection, segmentation and analysis based on in vivo cellular resolution images obtained with the transscleral optical phase imaging method (TOPI). METHODS: Fourteen TOPI-RPE images from 11 healthy individuals were analysed. The developed image processing method encompassed image filtering and normalisation, detection and removal of blood vessels, cell detection and cell membrane segmentation. The produced measures were cellular density of RPE layer, cell area, number of neighbouring cells, eccentricity, circularity and solidity. In addition, we proposed coefficient of variation (CV) of RPE cellular membrane (CMDCV) and the solidity of the RPE cell membrane-shape as new metrics for the assessment of RPE single cells. RESULTS: The observed median cellular density of the RPE layer was 3743 cells/µm2 (interquartile rate (IQR) 1687), with a median observed RPE cell area of 193 µm2 (IQR 141). The mean number of neighbouring cells was 5.22 (standard deviation (SD) 0.05) per RPE cell. The mean RPE cell eccentricity was 0.67 (SD 0.02), median circularity 0.83 (IQR 0.01), and median solidity 0.92 (IQR 0.00). The median CMDCV was 0.19 (IQR 0.02). The method is characterised by a median image processing and analysis time of 48 sec (IQR 12) per image. CONCLUSIONS: The present study provides the first fully automated quantitative assessment of human RPE single cells in vivo. The method provides a baseline for future research in the field of clinical ophthalmology, enabling characterisation and diagnostics of retinal diseases at the single-cell level.

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The phase sensitivity limit of Differential Phase Contrast (DPC) with partially coherent light is analyzed in details. The parameters to tune phase sensitivity, such as the diameter of illumination, the numerical aperture of the objective, and the noise of the camera are taken into account to determine the minimum phase contrast that can be detected. We found that a priori information about the sample can be used to fine-tune these parameters to increase phase contrast. Based on this information, we propose a simple algorithm to predict phase sensitivity of a DPC setup, which can be performed before the setup is built. Experiments confirm the theoretical findings.

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The observation of retinal cellular structures is fundamental to the understanding of eye pathologies. However, except for rods and cones, most of the retinal microstructures are weakly reflective and thus difficult to image with state of the art reflective optical imaging techniques such as optical coherence tomography. Recently, we demonstrated the possibility of obtaining the phase contrast of retinal cells in the eye using oblique illumination of the retina. Indeed, by illuminating the eye with incoherent oblique illumination, we obtain a secondary oblique illumination from the backscattered light which can then be used to obtain phase contrast in an effective transmission-like configuration. In this technique, a weak phase signal is modulated over an intense background. Maximizing this phase contrast is thus crucial for the image quality. Here, we investigate the parameters that affect phase contrast by modelling image formation with the backscattered light. We find that the key parameter for maximizing contrast is the intensity profile of the backscattered light. Specifically, the gradient of the profile is found to be proportional to the phase contrast. We validate the model by comparing simulations with experimental results on ex-vivo retina samples.

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