Abstract
Quantification of the human rod and cone photoreceptor mosaic in adaptive optics scanning light ophthalmoscope (AOSLO) images is useful for the study of various retinal pathologies. Subjective and time-consuming manual grading has remained the gold standard for evaluating these images, with no well validated automatic methods for detecting individual rods having been developed. We present a novel deep learning based automatic method, called the rod and cone CNN (RAC-CNN), for detecting and classifying rods and cones in multimodal AOSLO images. We test our method on images from healthy subjects as well as subjects with achromatopsia over a range of retinal eccentricities. We show that our method is on par with human grading for detecting rods and cones.
Highlights
Analysis of rod and cone photoreceptors is valuable for the study, diagnosis, and prognosis of various retinal diseases
The high resolution of adaptive optics (AO) ophthalmoscopes enables the visualization of photoreceptors in the living human retina [1,2], and these AO ophthalmoscopes have been used to study the properties of cones [3,4,5,6,7,8] and rods [6,8,9,10] in both healthy and pathological eyes
We show that our method is able to accurately localize and classify rods and cones in AO scanning light ophthalmoscope (AOSLO) images from both healthy subjects and from those with ACHM
Summary
Analysis of rod and cone photoreceptors is valuable for the study, diagnosis, and prognosis of various retinal diseases. The high resolution of adaptive optics (AO) ophthalmoscopes enables the visualization of photoreceptors in the living human retina [1,2], and these AO ophthalmoscopes have been used to study the properties of cones [3,4,5,6,7,8] and rods [6,8,9,10] in both healthy and pathological eyes. The most widely used AO ophthalmic technology is the AO scanning light ophthalmoscope (AOSLO), due to the superior image contrast provided by its axial sectioning, and its potential higher transverse resolution [1,12,13,14,15,16]. Split detector AOSLO has a reduced ability to visualize rods in comparison to confocal AOSLO, but Corrected: 31 Jul 2019
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