We focus on the practical challenge of segmenting new retinal fundus images that are dissimilar to existing well-annotated datasets. It is addressed in this paper by a supervised learning pipeline, with its core being the construction of a synthetic fundus image dataset using the proposed R-sGAN technique. The resulting synthetic images are realistic-looking in terms of the query images, while maintaining the annotated vessel structures from the existing dataset. This helps to bridge the mismatch between the query images and the existing well-annotated dataset.
IEEE Transactions on Medical Imaging, 2018 Jul 24. doi: 10.1109/TMI.2018.2854886