Abstract
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.
Keywords
- Retinal image
- Vessel segmentation
- Eye
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Acknowledgments
This work was partly funded by the ADAPT Centre for Digital Content Technology, which is funded under the SFI Research Centres Programme (13/RC/2106_P2) and is cofunded by the European Regional Development Fund, and also partly supported by Department of Nephrology, St. James’s Hospital, Dublin Ireland. Dr. Donal J. Sexton is funded by Health Research Board of Ireland: grant number ARPP-P-2018-011.
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Karaali, A., Dahyot, R., Sexton, D.J. (2022). DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_17
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