Karaali, Ali and Dahyot, Rozenn and Sexton, Donal J (2022) DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet. Lecture Notes in Computer Science, 13363. pp. 198-210. ISSN 0302-9743

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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.

Item Type:	Article
Keywords:	Retinal image; Vessel segmentation; Eye;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	18359
Depositing User:	Rozenn Dahyot
Date Deposited:	09 Apr 2024 14:33
Journal or Publication Title:	Lecture Notes in Computer Science
Publisher:	Springer Verlag
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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