Adversarial synthesis of retinal images from vessel trees

Authors: Costa, P., Galdran, A., Meyer, M.I., Mendonça, A.M. and Campilho, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 10317 LNCS

Pages: 516-523

eISSN: 1611-3349

ISBN: 9783319598758

ISSN: 0302-9743

DOI: 10.1007/978-3-319-59876-5_57

Abstract:

Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. Here we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality.

Source: Scopus