Repeatability of in vivo parafoveal cone density and spacing measurements

Abstract

Purpose: To assess the repeatability and measurement error associated with cone density and nearest neighbor distance (NND) estimates in images of the parafoveal cone mosaic obtained with an adaptive optics scanning light ophthalmoscope (AOSLO).

Methods: Twenty-one participants with no known ocular pathology were recruited. Four retinal locations, approximately 0.65° eccentricity from the center of fixation, were imaged 10 times in randomized order with an AOSLO. Cone coordinates in each image were identified using an automated algorithm (with or without manual correction) from which cone density and NND were calculated. Owing to naturally occurring fixational instability, the 10 images recorded from a given location did not overlap entirely. We thus analyzed each image set both before and after alignment.

Results: Automated estimates of cone density on the unaligned image sets showed a coefficient of repeatability of 11,769 cones/mm(2) (17.1%). The primary reason for this variability appears to be fixational instability, as aligning the 10 images to include the exact same retinal area results in an improved repeatability of 4358 cones/mm(2) (6.4%) using completely automated cone identification software. Repeatability improved further by manually identifying cones missed by the automated algorithm, with a coefficient of repeatability of 1967 cones/mm(2) (2.7%). NND showed improved repeatability and was generally insensitive to the undersampling by the automated algorithm.

Conclusions: As our data were collected in a young, healthy population, this likely represents a best-case estimate for corresponding measurements in patients with retinal disease. Similar studies need to be carried out on other imaging systems (including those using different imaging modalities, wavefront correction technology, and/or image analysis software), as repeatability would be expected to be highly sensitive to initial image quality and the performance of cone identification algorithms. Separate studies addressing intersession repeatability and interobserver reliability are also needed.

Figure 1

Parafoveal imaging locations used in this study. Shown is a foveal montage from subject JC_0645. Montages were not created for each subject, this one is presented simply to assist with understanding the relationship between the size of the scanning raster and that of the sampled areas for density analysis as well as the relationship between the foveal center and the location of the parafoveal sampling locations. The large white box represents the extent of the AOSLO scanning raster (0.96° x 0.96°), with the approximate location of the foveal center (fixation) marked with a white circle at the center of the box. The subject was asked to fixate at each of the four corners of the scanning square, and the central portion of each of these images was cropped for density analysis, indicated by the smaller white squares. In this illustration, the small white squares are 55μm × 55μm in size. Scale bar is 100μm.

Figure 2

Effect of fixation instability on the retinal area sampled across the 10 images for a given retinal location. Shown are unaligned (left) and aligned (right) image sequences of the 10 images acquired using the temporal-inferior fixation location for JC_0616. The white box depicts a 55μm × 55μm sampling window, demonstrating how different photoreceptors are sampled in each of the 10 images in the unaligned condition, while in the aligned image sequence, the exact same photoreceptors are analyzed in each of the 10 images. See Supplemental Digital Content 1 (available at [LWW insert link]) for the full video sequences. Scale bar is 50μm.

Figure 3

Cone photoreceptor images for all 21 subjects, acquired using the temporal-superior fixation location. So as not to bias the reader, the representative image for each subject was chosen randomly from the 10 images from this location. Scale bar is 25μm.

Figure 4

Variable performance of the automated cone identification algorithm. Shown are images from three subjects, JC_0659, JC_0656, and JC_0654. These images illustrate the variable performance of the automated algorithm across all 840 images analyzed in the aligned case. In the image from JC_0659 the algorithm missed no cones, while in the image from JC_0654, the user added 62 cones. The average number of cones added manually across all images was 12 (5.5%), which is the number missed by the automated algorithm in the image from JC_0656. Yellow circles represent cones identified by the automatic algorithm; pink cones indicate those added by the user during the manual addition step. All images are 55 μm × 55μm in size.