Methods: Areas of GA were delineated by 4 to 5 experienced graders in consensus in 377 CF images (252 eyes) collected from the Rotterdam Study and the Blue Mountains Eye Study. Graders made use of multimodal and follow up images when available, using our EyeNED annotation workstation. We identified 84 pairs of images (baseline and follow-up) of the same eye that were acquired with an interval of approximately 5 years. Image registration was performed by identifying corresponding landmarks between the images, allowing to project the delineated GA of the follow-up image onto the baseline image. Next, a fully automatic segmentation model, based on a deep CNN, was developed. The CNN was trained to simultaneously segment the current GA area and the area at risk of developing GA, using only the baseline image as input. A five-fold cross-validation was performed to validate the prediction performance.
Results: The model achieved an average dice coefficient of 0.63 for segmentation of areas at risk of developing GA in the 84 images. The intraclass correlation coefficient between the GA area defined by the consensus grading of the follow-up image and the automatically predicted area based on the baseline image was 0.54.
Conclusions: We present a model based on a deep CNN that is capable of identifying areas where GA may develop from CF images. The proposed approach constitutes a step towards personalized prognosis and possible treatment decisions. Furthermore, the model may be used for automatic discovery of new predictive biomarkers for development and growth rate of GA, and may help to automatically identify individuals at risk of developing GA.