Computer-assisted photoreceptor assessment on Heidelberg Engineering Spectralis™ High Magnification Module™ images.

PURPOSE: To evaluate reliability and repeatability of computer-assisted measurements of cone photoreceptor metrics on Heidelberg Engineering Spectralis™ High Magnification Module (HMM™) Automatic Real-time Tracking (ART™) images. METHODS: We analyzed HMM™ images in three separate study arms. Computer-assisted cone identification software was validated using an open-access adaptive optics (AO) dataset. We compared results of the first arm to data from AO and histology. We evaluated intersession repeatability of our computer-assisted cone analysis in the second arm. We assessed the capability of HMM™ to visualize cones in the presence of pathology in the third arm. RESULTS: We included 10 healthy subjects in the first arm of our study, 5 additional healthy participants in the second arm and 5 patients in the third arm. In total, we analyzed 225 regions of interest on HMM™ images. We were able to automatically identify cone photoreceptors and assess corresponding metrics at all eccentricities between 2 and 9° from the fovea. Cone density significantly declined with increasing eccentricity (p = 4.890E-26, Friedman test). With increasing eccentricity, we found a significant increase in intercell distance (p = 2.196E-25, Friedman test) and nearest neighbor distance (p = 1.997E-25, Friedman test). Cone hexagonality ranged between 71 and 85%. We found excellent automated intersession repeatability of cone density counts and spacing measurements. In pathology, we were also able to repeatedly visualize photoreceptors. CONCLUSION: Computer-assisted cone photoreceptor analysis on Spectralis™ HMM™ images is feasible, and most cone metrics show excellent repeatability. HMM™ imaging may be useful for photoreceptor analysis as progression marker in outer retinal disease.

Methods for investigating the local spatial anisotropy and the preferred orientation of cones in adaptive optics retinal images.

The ability to noninvasively image the cone photoreceptor mosaic holds significant potential as a diagnostic for retinal disease. Central to the realization of this potential is the development of sensitive metrics for characterizing the organization of the mosaic. Here we evaluated previously-described and newly-developed (Fourier- and Radon-based) methods of measuring cone orientation in simulated and real images of the parafoveal cone mosaic. The proposed algorithms correlated well across both simulated and real mosaics, suggesting that each algorithm provides an accurate description of photoreceptor orientation. Despite high agreement between algorithms, each performed differently in response to image intensity variation and cone coordinate jitter. The integration property of the Fourier transform allowed the Fourier-based method to be resistant to cone coordinate jitter and perform the most robustly of all three algorithms. Conversely, when there is good image quality but unreliable cone identification, the Radon algorithm performed best. Finally, in cases where the cone coordinate reliability was excellent, the method previously described by Pum and colleagues performed best. These descriptors are complementary to conventional descriptive metrics of the cone mosaic, such as cell density and spacing, and have the potential to aid in the detection of photoreceptor pathology.