In vivo dark-field imaging of the retinal pigment epithelium cell mosaic

Department of Biomedical Engineering, University of Rochester, Rochester, NY, 14627, USA.
Biomedical Optics Express (Impact Factor: 3.65). 09/2013; 4(9):1710-23. DOI: 10.1364/BOE.4.001710
Source: PubMed


Non-invasive reflectance imaging of the human RPE cell mosaic is demonstrated using a modified confocal adaptive optics scanning light ophthalmoscope (AOSLO). The confocal circular aperture in front of the imaging detector was replaced with a combination of a circular aperture 4 to 16 Airy disks in diameter and an opaque filament, 1 or 3 Airy disks thick. This arrangement reveals the RPE cell mosaic by dramatically attenuating the light backscattered by the photoreceptors. The RPE cell mosaic was visualized in all 7 recruited subjects at multiple retinal locations with varying degrees of contrast and cross-talk from the photoreceptors. Various experimental settings were explored for improving the visualization of the RPE cell boundaries including: pinhole diameter, filament thickness, illumination and imaging pupil apodization, unmatched imaging and illumination focus, wavelength and polarization. None of these offered an obvious path for enhancing image contrast. The demonstrated implementation of dark-field AOSLO imaging using 790 nm light requires low light exposures relative to light safety standards and it is more comfortable for the subject than the traditional autofluorescence RPE imaging with visible light. Both these factors make RPE dark-field imaging appealing for studying mechanisms of eye disease, as well as a clinical tool for screening and monitoring disease progression.

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    Biomedical Optics Express 11/2013; 4(11):2683-2695. DOI:10.1364/BOE.4.002683 · 3.65 Impact Factor
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    ABSTRACT: Adaptive optics (AO) imaging methods allow the histological characteristics of retinal cell mosaics, such as photoreceptors and retinal pigment epithelium (RPE) cells, to be studied in vivo. The high-resolution images obtained with ophthalmic AO imaging devices are rich with information that is difficult and/or tedious to quantify using manual methods. Thus, robust, automated analysis tools that can provide reproducible quantitative information about the cellular mosaics under examination are required. Automated algorithms have been developed to detect the position of individual photoreceptor cells; however, most of these methods are not well suited for characterizing the RPE mosaic. We have developed an algorithm for RPE cell segmentation and show its performance here on simulated and real fluorescence AO images of the RPE mosaic. Algorithm performance was compared to manual cell identification and yielded better than 91% correspondence. This method can be used to segment RPE cells for morphometric analysis of the RPE mosaic and speed the analysis of both healthy and diseased RPE mosaics.
    Journal of the Optical Society of America A 12/2013; 30(12):2595. DOI:10.1364/JOSAA.30.002595 · 1.56 Impact Factor
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