Skip to main content

End-to-End Adversarial Retinal Image Synthesis.

Costa, P., Galdran, A., Meyer, M.I., Niemeijer, M., Abràmoff, M., Mendonça, A.M. and Campilho, A., 2018. End-to-End Adversarial Retinal Image Synthesis. IEEE transactions on medical imaging, 37 (3), 781 - 791.

Full text available as:

tmi_2017.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1109/TMI.2017.2759102


In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.

Item Type:Article
Uncontrolled Keywords:Algorithms ; Diagnostic Techniques, Ophthalmological ; Humans ; Image Processing, Computer-Assisted ; Neural Networks, Computer ; Retina ; Retinal Vessels
Group:Faculty of Science & Technology
ID Code:34883
Deposited By: Symplectic RT2
Deposited On:25 Nov 2020 12:04
Last Modified:14 Mar 2022 14:25


Downloads per month over past year

More statistics for this item...
Repository Staff Only -