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For medical diagnosis based on retinal images, a clear understanding of 3Dstructure is often required but due to the 2D nature of images captured, wecannot infer that information. However, by utilizing 3D reconstruction methods,we can recover the height information of the macula area on a fundus imagewhich can be helpful for diagnosis and screening of macular disorders. Recentapproaches have used shading information for heightmap prediction but theiroutput was not accurate since they ignored the dependency between nearby pixelsand only utilized shading information. Additionally, other methods weredependent on the availability of more than one image of the retina which is notavailable in practice. In this paper, motivated by the success of ConditionalGenerative Adversarial Networks(cGANs) and deeply supervised networks, wepropose a novel architecture for the generator which enhances the details andthe quality of output by progressive refinement and the use of deep supervisionto reconstruct the height information of macula on a color fundus image.Comparisons on our own dataset illustrate that the proposed method outperformsall of the state-of-the-art methods in image translation and medical imagetranslation on this particular task. Additionally, perceptual studies alsoindicate that the proposed method can provide additional information forophthalmologists for diagnosis.

Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning