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Accurate retinal vessel segmentation is an important task for manycomputer-aided diagnosis systems. Yet, it is still a challenging problem due tothe complex vessel structures of an eye. Numerous vessel segmentation methodshave been proposed recently, however more research is needed to deal with poorsegmentation of thin and tiny vessels. To address this, we propose a new deeplearning pipeline combining the efficiency of residual dense net blocks and,residual squeeze and excitation blocks. We validate experimentally our approachon three datasets and show that our pipeline outperforms current state of theart techniques on the sensitivity metric relevant to assess capture of smallvessels.

DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet