Retinal vessel segmentation is a fundamental step in screening, diagnosis, and treatment of various cardiovascular ophthalmic diseases. Robustness one the most critical requirements for practical utilization, since test images may be captured using different fundus cameras, or affected by pathological changes. We investigate this problem from data augmentation perspective, with merits no additi...

Fundus imaging is one of the crucial methods that help ophthalmologists for diagnosing various eye diseases in modern medicine. An accurate vessel segmentation method can be a convenient tool to foresee and analyze fatal diseases, including hypertension or diabetes, which damage retinal vessel’s appearance. This work suggests an unsupervised approach vessels out images. The proposed includes mu...

Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems. Yet, it still a challenging problem due to the complex structures of eye. Numerous methods have been proposed recently, however more research needed deal with poor thin and tiny vessels. To address this, we propose new deep learning pipeline combining efficiency residual dense net blocks and, sq...