Optic disc and fovea localisation in ultra-widefield scanning laser ophthalmoscope images captured in multiple modalities
This work addresses the need for automated localization in ophthalmology, particularly for ultra-widefield images, but it appears incremental as it applies an existing method (CNN) to a new imaging modality.
The paper tackled the problem of localizing the optic disc and fovea in ultra-widefield retinal images using a convolutional neural network, achieving accuracies of 99.4% and 99.1% respectively within one optic disc radius on a test set of 1790 images.
We propose a convolutional neural network for localising the centres of the optic disc (OD) and fovea in ultra-wide field of view scanning laser ophthalmoscope (UWFoV-SLO) images of the retina. Images captured in both reflectance and autofluorescence (AF) modes, and central pole and eyesteered gazes, were used. The method achieved an OD localisation accuracy of 99.4% within one OD radius, and fovea localisation accuracy of 99.1% within one OD radius on a test set comprising of 1790 images. The performance of fovea localisation in AF images was comparable to the variation between human annotators at this task. The laterality of the image (whether the image is of the left or right eye) was inferred from the OD and fovea coordinates with an accuracy of 99.9%