Analysis of foveal characteristics and their asymmetries in the normal population

Scheibe, Patrick; Zocher, Maria Teresa; Francke, Mike; Rauscher, Franziska G.

doi:10.1016/j.exer.2016.05.013

Cited by 17 publications

(23 citation statements)

References 31 publications

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“…In the initial model, a single peripheral point was clustered together with the central four points corresponding with the fovea, which is inconsistent with an a priori assumption that the foveal pit is anatomically distinct from the rest of the retina. 44 Thus, in developing the final model, the central four locations corresponding to the fovea were masked and the data were reanalyzed, resulting in the reassignment of the aforementioned peripheral point with no other change in the classification. The ISODATA algorithm and statistical analysis provided the maximum number of statistically separable classes.…”

Section: Methodsmentioning

confidence: 99%

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

Yoshioka

Zangerl

Nivison‐Smith

et al. 2017

Invest. Ophthalmol. Vis. Sci.

109

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PurposeTo characterize macular ganglion cell layer (GCL) changes with age and provide a framework to assess changes in ocular disease. This study used data clustering to analyze macular GCL patterns from optical coherence tomography (OCT) in a large cohort of subjects without ocular disease.MethodsSingle eyes of 201 patients evaluated at the Centre for Eye Health (Sydney, Australia) were retrospectively enrolled (age range, 20–85); 8 × 8 grid locations obtained from Spectralis OCT macular scans were analyzed with unsupervised classification into statistically separable classes sharing common GCL thickness and change with age. The resulting classes and gridwise data were fitted with linear and segmented linear regression curves. Additionally, normalized data were analyzed to determine regression as a percentage. Accuracy of each model was examined through comparison of predicted 50-year-old equivalent macular GCL thickness for the entire cohort to a true 50-year-old reference cohort.ResultsPattern recognition clustered GCL thickness across the macula into five to eight spatially concentric classes. F-test demonstrated segmented linear regression to be the most appropriate model for macular GCL change. The pattern recognition–derived and normalized model revealed less difference between the predicted macular GCL thickness and the reference cohort (average ± SD 0.19 ± 0.92 and −0.30 ± 0.61 μm) than a gridwise model (average ± SD 0.62 ± 1.43 μm).ConclusionsPattern recognition successfully identified statistically separable macular areas that undergo a segmented linear reduction with age. This regression model better predicted macular GCL thickness. The various unique spatial patterns revealed by pattern recognition combined with core GCL thickness data provide a framework to analyze GCL loss in ocular disease.

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Section: Methodsmentioning

confidence: 99%

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

Yoshioka

Zangerl

Nivison‐Smith

et al. 2017

Invest. Ophthalmol. Vis. Sci.

109

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“…As pointed out in Ref. 51, p. 5, instead of using of the highest point of the foveal rim for this purpose, it is more advantageous to solve the equation E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 5 ; 3 2 6 ; 2 8 8…”

Section: Definition Of the Foveal Region Within The B-scansmentioning

confidence: 99%

“…Based on these segmentations, we provide an automated determination of the central fovea region. 50,51 Automated measurements of mean choroidal thickness are given for this and two adjacent 1 mm regions, as defined in more detail in Sec. 2.6.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of the choroid boundary within OCT image data using quadratic measure filters

et al. 2017

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Abstract. A novel method for the automated detection of the outer choroid boundary within spectral-domain optical coherence tomography image data, based on an image model within the space of functions of bounded variation and the application of quadratic measure filters, is presented. The same method is used for the segmentation of retinal layer boundaries and proves to be suitable even for data generated without special imaging modes and moderate line averaging. Based on the segmentations, an automated determination of the central fovea region and choroidal thickness measurements for this and two adjacent 1-mm regions are provided. The quality of the method is assessed by comparison with manual delineations performed by five trained graders. The study is based on data from 50 children of the ages 8 to 13 that were obtained in the framework of the LIFE Child study at Leipzig University.

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“…In our approach, an optically homogeneous tissue with a thickness of 250 μm and a refractive index of n retina = 1.370 at 555 nm wavelength with an Abbe's number of 50.23 was assumed for the retinal layer [1,12]. Thus, the length of the vitreous was reduced accordingly.…”

Section: Optical Simulation Basismentioning

confidence: 99%

“…Because of the refractive index step between the retinal tissue and vitreous, the fovea defines an optically effective interface with aspherical characteristics, which influences the ray propagation to the receptor layer buried about 250 μm within the retinal layers [1]. In principle, two basic foveal shapes can be distinguished [2].…”

Section: Introductionmentioning

confidence: 99%

Does the foveal shape influence the image formation in human eyes?

Frey

Zimmerling

Scheibe

et al. 2017

Advanced Optical Technologies

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Abstract:In human eyes, the maximum visual acuity correlates locally with the fovea, a shallow depression in the retina. Previous examinations have been reduced to simple geometrical fovea models derived from postmortem preparations and considering only a few superficial ray propagation aspects. In the current study, an extended and realistic analysis of ray-optical simulations for a comprehensive anatomical realistic eye model for the anterior part and realistic aspherical human foveal topographical profiles deduced from in vivo optical coherence tomography (OCT) are presented, and the refractive index step at the transition from vitreous to retinal tissue is taken into account. The optical effect of a commonly shaped (averaged) and an extraordinarily shaped foveal pit were both compared to the analysis of an assumed pure spherical boundary layer. The influence of the aperture size, wavelength, and incident angle on the spot size and shape, as well as the axial focal and lateral centroid position is investigated, and a lateral displacement of about 2 μm and an axial shift of the best focal position of less than 4 μm are found. These findings indicate only small optical effects that are laterally in the range of inter-receptor distances and axially less than the photoreceptor outer segment dimension.

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Analysis of foveal characteristics and their asymmetries in the normal population

Cited by 17 publications

References 31 publications

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

Pattern Recognition Analysis of Age-Related Retinal Ganglion Cell Signatures in the Human Eye

Automated detection of the choroid boundary within OCT image data using quadratic measure filters

Does the foveal shape influence the image formation in human eyes?

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