Abstract
Widespread screening is crucial for the early diagnosis and treatment of glaucoma, the leading cause of visual impairment and blindness. The development of portable technologies, such as smartphone-based ophthalmoscopes, able to image the optical nerve head, represents a resource for large-scale glaucoma screening. Indeed, they consist of an optical device attached to a common smartphone, making the overall device cheap and easy to use. Automated analyses able to assist clinicians are crucial for fast, reproducible, and accurate screening, and can promote its diffusion making it possible even for non-expert ophthalmologists. Images acquired with smartphone ophthalmoscopes differ from that acquired with a fundus camera for the field of view, noise, colour, and the presence of pupil, iris and eyelid. Consequently, algorithms specifically designed for this type of image need to be developed. We propose a completely automated analysis of retinal video acquired with smartphone ophthalmoscopy. The proposed algorithm, based on convolutional neural networks, selects the most relevant frames in the video, segments both optic disc and cup, and computes the cup-to-disc ratio. The developed networks were partially trained on images from a publicly available fundus camera datasets, modified through an original procedure to be statistically equal to the ones acquired with a smartphone ophthalmoscope. The proposed algorithm achieves good results in images acquired from healthy and pathological subjects. Indeed, an accuracy ≥95 % was obtained for both disc and cup segmentation and the computed cup-to-disc ratios denote good agreement with manual analysis (mean difference 9 %), allowing a substantial differentiation between healthy and pathological subjects.