**1. Introduction**

The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases, such as diabetes, age-related macular degeneration (AMD) and glaucoma [1]. This is due to the presence of these diseases causing changes in the retinal vessels. An exhaustive analysis of the retinal vasculature involves segmenting the vascular tree and classifying their vessels into veins and arteries. Despite its utility, this type of analysis is rarely applied in clinical practice, as performing it manually is arduous, and often leads to partly subjective results. For this reason, several automatic methods have been proposed. Early methods addressed these tasks into two sequential steps [2]. However, this approach causes the classification results to be highly conditioned by the segmentation results. To overcome this issue, the current state of the art (SOTA) addresses both tasks as a single multi-class semantic segmentation problem [3–6].

In this work, we present an accurate approach, based on deep neural networks, for the joint segmentation and classification of the retinal arteries and veins (JSCAV) from color fundus images. This approach, differently to SOTA, decomposes the joint task into three subtasks: the segmentation of arteries, veins and the whole vascular tree. In the following sections, we discuss this approach and its associated advantages.
