Abstract
Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model.
Highlights
Diabetic Retinopathy (DR) is a disease in which micro-blood vessels in the retina are damaged, and related blood vessel damage is one of the most fatal complications of diabetes
Image Synthesis of the Fundus Generator: Once the fundus generator is trained, we can synthesize the realistic fundus images, which can be coarsely predicted from the corresponding grading images, and control the pathological information by switching the lesion mask
We observe that the fundus generator can synthesize fundus images that reflect the lesion information and vessel structure within the corresponding two masks at the same position
Summary
Diabetic Retinopathy (DR) is a disease in which micro-blood vessels in the retina are damaged, and related blood vessel damage is one of the most fatal complications of diabetes. Bleeding or edema in the central part of the retina (macula), where the visual cells are concentrated and projected images are in focus, can lead to severe vision loss and blindness. All patients with this DR disease potentially have a DME symptom. Predicting the occurrence of symptoms of DME in advance is important in predicting future DR severity from current information to preserve the vision of patients
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