Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network.

Abstract

PurposeAdaptive optics imaging has enabled the visualization of photoreceptors both in health and disease. However, there remains a need for automated accurate cone photoreceptor identification in images of disease. Here, we apply an open-source convolutional neural network (CNN) to automatically identify cones in images of choroideremia (CHM). We further compare the results to the repeatability and reliability of manual cone identifications in CHM.MethodsWe used split-detection adaptive optics scanning laser ophthalmoscopy to image the inner segment cone mosaic of 17 patients with CHM. Cones were manually identified twice by one experienced grader and once by two additional experienced graders in 204 regions of interest (ROIs). An open-source CNN either pre-trained on normal images or trained on CHM images automatically identified cones in the ROIs. True and false positive rates and Dice's coefficient were used to determine the agreement in cone locations between data sets. Interclass correlation coefficient was used to assess agreement in bound cone density.ResultsIntra- and intergrader agreement for cone density is high in CHM. CNN performance increased when it was trained on CHM images in comparison to normal, but had lower agreement than manual grading.ConclusionsManual cone identifications and cone density measurements are repeatable and reliable for images of CHM. CNNs show promise for automated cone selections, although additional improvements are needed to equal the accuracy of manual measurements.Translational RelevanceThese results are important for designing and interpreting longitudinal studies of cone mosaic metrics in disease progression or treatment intervention in CHM.