Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

Xue, Bai; Choi, Stacey S.; Doble, Nathan; Werner, John S.

doi:10.1364/josaa.24.001364

Cited by 66 publications

(79 citation statements)

References 30 publications

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“…Most studies have focused on the analysis of the spatial distribution of the cones [1][2][3][4], showing how the characteristics of the cone mosaic can vary between subjects, who are either healthy or who present retinal disease [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Mariotti¹,

Devaney²,

Lombardo³

et al. 2016

Biomed. Opt. Express

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Abstract:Although there is increasing interest in the investigation of cone reflectance variability, little is understood about its characteristics over long time scales. Cone detection and its automation is now becoming a fundamental step in the assessment and monitoring of the health of the retina and in the understanding of the photoreceptor physiology. In this work we provide an insight into the cone reflectance variability over time scales ranging from minutes to three years on the same eye, and for large areas of the retina (≥ 2.0 × 2.0 degrees) at two different retinal eccentricities using a commercial adaptive optics (AO) flood illumination retinal camera. We observed that the difference in reflectance observed in the cones increases with the time separation between the data acquisitions and this may have a negative impact on algorithms attempting to track cones over time. In addition, we determined that displacements of the light source within 0.35 mm of the pupil center, which is the farthest location from the pupil center used by operators of the AO camera to acquire high-quality images of the cone mosaic in clinical studies, does not significantly affect the cone detection and density estimation.

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Section: Introductionmentioning

confidence: 99%

Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Mariotti¹,

Devaney²,

Lombardo³

et al. 2016

Biomed. Opt. Express

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“…We evaluated three methods of automatically sampling cone density, assessed the repeatability of our measurements, and compared our cone density values to histological studies, 44,45 adding our data to previous studies that have used AO imaging systems to characterize the normal photoreceptor mosaic. 37,38,[46][47][48][49][50][51][52] University Institutional Review Board. We tested the dominant eye of 74 healthy subjects ranging in age from 14 to 69 years.…”

mentioning

confidence: 99%

“…The algorithm first estimated and then removed image backgrounds with an 11 3 11 moving average filter 46 and then detected cones by thresholding the intensity values of the local maxima 53 (Fig. 1D).…”

mentioning

confidence: 99%

Assessment of Different Sampling Methods for Measuring and Representing Macular Cone Density Using Flood-Illuminated Adaptive Optics

Feng

Gale

Fay

et al. 2015

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. To describe a standardized flood-illuminated adaptive optics (AO) imaging protocol suitable for the clinical setting and to assess sampling methods for measuring cone density.METHODS. Cone density was calculated following three measurement protocols: 50 3 50-lm sampling window values every 0.58 along the horizontal and vertical meridians (fixed-interval method), the mean density of expanding 0.58-wide arcuate areas in the nasal, temporal, superior, and inferior quadrants (arcuate mean method), and the peak cone density of a 50 3 50-lm sampling window within expanding arcuate areas near the meridian (peak density method). Repeated imaging was performed in nine subjects to determine intersession repeatability of cone density. RESULTS.Cone density montages could be created for 67 of the 74 subjects. Image quality was determined to be adequate for automated cone counting for 35 (52%) of the 67 subjects. We found that cone density varied with different sampling methods and regions tested. In the nasal and temporal quadrants, peak density most closely resembled histological data, whereas the arcuate mean and fixed-interval methods tended to underestimate the density compared with histological data. However, in the inferior and superior quadrants, arcuate mean and fixed-interval methods most closely matched histological data, whereas the peak density method overestimated cone density compared with histological data. Intersession repeatability testing showed that repeatability was greatest when sampling by arcuate mean and lowest when sampling by fixed interval. CONCLUSIONS.We show that different methods of sampling can significantly affect cone density measurements. Therefore, care must be taken when interpreting cone density results, even in a normal population.

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“…Later, closed loop AO systems were developed with faster and automated wavefront sensing and dynamic correction of ocular aberrations (Hofer, 2001;Fernandez 2001). To date, AO has been successfully combined with ophthalmic imaging systems such as flood illuminated fundus camera, scanning laser ophthalmoscope (SLO) and OCT systems for improved lateral resolution and image contrast (Choi et al, 2005;Choi et al, 2006;Jonnal et al, 2007;Roorda et al, 2002a;Roorda et al, 2002b;Thorn et al, 2003;Wolfing et al,2006;Xue et al,2007).…”

Section: Adaptive Optics (Ao)mentioning

confidence: 99%

Cellular Level Imaging of the Retina Using Optical Coherence Tomography

Greiner¹,

Choi²

2012

Selected Topics in Optical Coherence Tomography

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Photoreceptor counting and montaging of en-face retinal images from an adaptive optics fundus camera

Cited by 66 publications

References 30 publications

Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Assessment of Different Sampling Methods for Measuring and Representing Macular Cone Density Using Flood-Illuminated Adaptive Optics

Cellular Level Imaging of the Retina Using Optical Coherence Tomography

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