DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet
This work addresses a specific challenge in retinal vessel segmentation for computer-aided diagnosis systems, representing an incremental improvement.
The paper tackled the problem of poor segmentation of thin and tiny retinal vessels by proposing a deep learning pipeline combining residual dense net and residual squeeze and excitation blocks, achieving state-of-the-art performance on sensitivity metrics across three datasets.
Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems. Yet, it is still a challenging problem due to the complex vessel structures of an eye. Numerous vessel segmentation methods have been proposed recently, however more research is needed to deal with poor segmentation of thin and tiny vessels. To address this, we propose a new deep learning pipeline combining the efficiency of residual dense net blocks and, residual squeeze and excitation blocks. We validate experimentally our approach on three datasets and show that our pipeline outperforms current state of the art techniques on the sensitivity metric relevant to assess capture of small vessels.