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

For medical diagnosis based on retinal images, a clear understanding of 3D structure is often required but due to the 2D nature of images captured, we cannot infer that information. However, by utilizing 3D reconstruction methods, we can construct the 3D structure of the macula area on fundus images which can be helpful for diagnosis and screening of macular disorders. Recent approaches have used shading information for 3D reconstruction or heightmap prediction but their output was not accurate since they ignored the dependency between nearby pixels. 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, we use conditional generative adversarial networks (cGANs) to generate images that contain height information of the macula area on a fundus image. Results using our dataset show a 0.6077 improvement in Structural Similarity Index (SSIM) and 0.071 improvements in Mean Squared Error (MSE) metric over Shape from Shading (SFS) method. Additionally, Qualitative studies also indicate that our method outperforms recent approaches.