  The study of the retinal vasculature is a fundamental stage in the screening
and diagnosis of many diseases. A complete retinal vascular analysis requires
to segment and classify the blood vessels of the retina into arteries and veins
(A/V). Early automatic methods approached these segmentation and classification
tasks in two sequential stages. However, currently, these tasks are approached
as a joint semantic segmentation task, as the classification results highly
depend on the effectiveness of the vessel segmentation. In that regard, we
propose a novel approach for the simultaneous segmentation and classification
of the retinal A/V from eye fundus images. In particular, we propose a novel
method that, unlike previous approaches, and thanks to a novel loss, decomposes
the joint task into three segmentation problems targeting arteries, veins and
the whole vascular tree. This configuration allows to handle vessel crossings
intuitively and directly provides accurate segmentation masks of the different
target vascular trees. The provided ablation study on the public Retinal Images
vessel Tree Extraction (RITE) dataset demonstrates that the proposed method
provides a satisfactory performance, particularly in the segmentation of the
different structures. Furthermore, the comparison with the state of the art
shows that our method achieves highly competitive results in A/V
classification, while significantly improving vascular segmentation. The
proposed multi-segmentation method allows to detect more vessels and better
segment the different structures, while achieving a competitive classification
performance. Also, in these terms, our approach outperforms the approaches of
various reference works. Moreover, in contrast with previous approaches, the
proposed method allows to directly detect the vessel crossings, as well as
preserving the continuity of A/V at these complex locations.
