  For screening, 3D shape of the eye retina often provides structural
information and can assist ophthalmologists to diagnose diseases. However,
fundus images which are one the most common screening modalities for retina
diagnosis lack this information due to their 2D nature. Hence, in this work, we
try to infer about this 3D information or more specifically its heights. 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.
