Contributed Session II: In-vivo classification of human cone photoreceptors reveals crystalline S-cone sub-mosaics in the central retina

Schleufer, Sierra; Pandiyan, Vimal Prabhu; Bharadwaj, Palash; Sabesan, Ramkumar

doi:10.1167/jov.22.3.14

Cited by 1 publication

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“…To remove unwanted structural differences that vary across the AO-OCT volume image (i.e., variations with retinal eccentricity), the neighbor differences for each structural feature were calculated by subtracting the mean of each cone's six nearest (hexagonal) neighbors of that feature from the central cone's value. This strategy also took advantage of the fact that S cones are sufficiently sparse and regularly spaced that they have only M and L neighbors [17], [18]. All cone measurements were normalized by converting to z-scores based on the distribution of values from the same retinal eccentricity before being fed into the classifier.…”

Section: Structural Based Cone Classificationmentioning

confidence: 99%

S-cone identification using AO-OCT cone structural measurements and support vector machine classifier

Bernucci²,

Liu³

et al. 2023

Ophthalmic Technologies XXXIII

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The high resolution of adaptive optics optical coherence tomography (AO-OCT) allows 3-dimensional imaging of individual cone photoreceptors in vivo. Histology has revealed that short-wavelength-sensitive (S) cones have distinct structural features compared with medium-wavelength-sensitive (M) and long-wavelength-sensitive (L) cones. Quantifying these structural features in images of living human retinas may provide a simpler and quicker method for identifying S cones than by imaging cone function (e.g., optoretinography). Here, we present a quantitative method for using AO-OCT measurements of cone structure in a support vector machine (SVM) classifier to identify individual S cones. For every cone cell, we measured six key structural parameters: inner segment length (ISL), outer segment length (OSL), inner segment / outer segment conjunction (IS/OS) diameter, cone outer-segment tip (COST) diameter, IS/OS reflectance, and COST reflectance. ISL and OSL were determined from depth differences between reflections of the external limiting membrane (ELM) and IS/OS, and IS/OS and COST, respectively. Each reflection’s depth was measured with sub-pixel accuracy using Gaussian interpolation; its diameter was measured using the gradient information from the en face projection at that depth. Among 6,398 analyzed cones in six subjects, we found S cones had significantly longer ISLs, shorter OSLs, and wider IS/OS diameters than did cones of other types. We used these structural differences in our SVM model to classify cone spectral types and compared results with cone optoretinography. In five of the six subjects, S cones were identified with F1 scores ranging from 0.78 to 0.93.

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Section: Structural Based Cone Classificationmentioning

confidence: 99%