  In ophthalmology, the study of the retinal microcirculation is a key issue in
the analysis of many ocular and systemic diseases, like hypertension or
diabetes. This motivates the research on improving the retinal vasculature
segmentation. Nowadays, Fully Convolutional Neural Networks (FCNs) usually
represent the most successful approach to image segmentation. However, the
success of these models is conditioned by an adequate selection and adaptation
of the architectures and techniques used, as well as the availability of a
large amount of annotated data. These two issues become specially relevant when
applying FCNs to medical image segmentation as, first, the existent models are
usually adjusted from broad domain applications over photographic images, and
second, the amount of annotated data is usually scarcer. In this work, we
present multimodal transfer learning-based approaches for retinal vascular
segmentation, performing a comparative study of recent FCN architectures. In
particular, to overcome the annotated data scarcity, we propose the novel
application of self-supervised network pretraining that takes advantage of
existent unlabelled multimodal data. The results demonstrate that the
self-supervised pretrained networks obtain significantly better vascular masks
with less training in the target task, independently of the network
architecture, and that some FCN architecture advances motivated for broad
domain applications do not translate into significant improvements over the
vasculature segmentation task.
