Heightmap Reconstruction of Macula on Color Fundus Images Using Conditional Generative Adversarial Networks

Abstract

For screening of eye retina, the information about elevations in different parts can assist ophthalmologists to diagnose diseases better. However, fundus images which are one of the most common screening modalities for retina diagnosis lack this information due to their 2D nature. Hence, in this work, we try to automatically reconstruct this height information from a single color fundus image. Recent approaches have used shading information for reconstructing the heights but their output is not accurate since the utilized information is not sufficient. Additionally, other methods were dependent on the availability of more than one image of the eye which is not available in practice. In this paper, motivated by the success of Conditional Generative Adversarial Networks(cGANs) and deeply supervised networks, we propose a novel architecture for the generator which enhances the details in a sequence of steps. Comparisons on our dataset illustrate that the proposed method outperforms all of the state-of-the-art methods in image translation and medical image translation on this particular task. Additionally, clinical studies also indicate that the proposed method can provide additional information for ophthalmologists for diagnosis.