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.
This paper aims at synthesizing multiple realistic-looking retinal (or neuronal) images from an unseen tubular structured annotation that contains the binary vessel (or neuronal) morphology. The generated phantoms are expected to preserve the same tubular structure, and resemble the visual appearance of the training images. Inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer, our approach enjoys several advantages.
We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this