Imaging microscopic structures in pathological retinas using a flood-illumination adaptive optics retinal camera

Viard, Clément; Gocho, Kiyoko; Lamory, B.; Pâques, Michel; Lévecq, Xavier; Château, Nicolas

doi:10.1117/12.874766

Cited by 28 publications

(38 citation statements)

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“…13 The rtx1 camera probes wavefront aberrations with a 750 nm super luminescent diode and a Shack-Hartmann detector (Haso 32-Eye; Imagine Eyes) and corrects them with a 52 actuators AO system operating in a closed loop. The fundus is illuminated with a temporally low coherent light emitting diode flashed flood source operating at 840 nm.…”

Section: Adaptive Optics Imaging Proceduresmentioning

confidence: 99%

Adaptive Optics Imaging of Geographic Atrophy

Gocho

Sarda

Falah

et al. 2013

Investigative Ophthalmology & Visual Science

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PURPOSE. To report the findings of en face adaptive optics (AO) near infrared (NIR) reflectance fundus flood imaging in eyes with geographic atrophy (GA). METHODS.Observational clinical study of AO NIR fundus imaging was performed in 12 eyes of nine patients with GA, and in seven controls using a flood illumination camera operating at 840 nm, in addition to routine clinical examination. To document short term and midterm changes, AO imaging sessions were repeated in four patients (mean interval between sessions 21 days; median follow up 6 months). RESULTS.As compared with scanning laser ophthalmoscope imaging, AO NIR imaging improved the resolution of the changes affecting the RPE. Multiple hyporeflective clumps were seen within and around GA areas. Time-lapse imaging revealed micrometric-scale details of the emergence and progression of areas of atrophy as well as the complex kinetics of some hyporeflective clumps. Such dynamic changes were observed within as well as outside atrophic areas.CONCLUSIONS. In eyes affected by GA, AO NIR imaging allows high resolution documentation of the extent of RPE damage. This also revealed that a complex, dynamic process of redistribution of hyporeflective clumps throughout the posterior pole precedes and accompanies the emergence and progression of atrophy. Therefore, these clumps are probably also a biomarker of RPE damage. AO NIR imaging may, therefore, be of interest to detect the earliest stages, to document the retinal pathology and to monitor the progression of GA. (ClinicalTrials.gov number, NCT01546181.)

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Section: Adaptive Optics Imaging Proceduresmentioning

confidence: 99%

Adaptive Optics Imaging of Geographic Atrophy

Gocho

Sarda

Falah

et al. 2013

Investigative Ophthalmology & Visual Science

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“…[27][28][29][30] We used the rtx1 flood-illuminated AO camera from Imagine Eyes to develop a protocol for imaging and sampling cone density from a large macular area in healthy subjects. Previous studies have used the rtx1 to examine retinal diseases, 12,21,22,[31][32][33][34][35][36] study the healthy eye, [37][38][39][40] and optimize AO imaging parameters. 41,42 However, the practicality and limitations of using the rtx1 and other commercial systems have not been well established.…”

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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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“…365 × 232 Figure 1: A mean image produced by [5] acquired the rtx1 camera [6] (left) and a detailed view of an artery (right). The size of each image is indicated below it and expressed in pixels.…”

Section: × 947mentioning

confidence: 99%

“…In the present study, the camera rtx1 [6] is used to acquire 2D images by flood illumination at 10Hz using a 850nm LED light source with a pixel-resolution of 0.8µm. Flood-illumination systems usually produce noisy images making walls hardly visible.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Retinal Arteries in Adaptive Optics Images

Lermé

Rossant

Bloch

et al. 2014

2014 22nd International Conference on Pattern Recognition

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Abstract-In this paper, we present a method for automatically segmenting the walls of retinal arteries in adaptive optics images. To the best of our knowledge, this is the first method addressing this problem in such images. To achieve this goal, we propose to model these walls as four curves approximately parallel to a common reference line located near the center of vessels. Once this line is detected, the curves are simultaneously positioned as close as possible to the borders of walls using an original tracking procedure to cope with deformations along vessels. Then, their positioning is refined using a deformable model embedding a parallelism constraint. Such an approach enables us to control the distance of the curves to their reference line and improve the robustness to image noise. This model was evaluated on healthy subjects by comparing the results against segmentations from physicians. Noticeably, the error introduced by this model is smaller or very near the inter-physicians error.

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Imaging microscopic structures in pathological retinas using a flood-illumination adaptive optics retinal camera

Cited by 28 publications

References 0 publications

Adaptive Optics Imaging of Geographic Atrophy

Adaptive Optics Imaging of Geographic Atrophy

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

Segmentation of Retinal Arteries in Adaptive Optics Images

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