View abstract

This abstract is assigned to session AOB Free Papers

Presentation type	Oral presentation
Title	LUNet: Deep Learning for the Segmentation of Arterioles and Venules in Fundus Images

Purpose	Automatic segmentation of the retinal arterioles and the venules (A/V) is essential in building an automatic pipeline to study the patient’s microvasculature. Nevertheless, there is a lack of a data-driven algorithm for A/V segmentation that may enable analyzing the patient’s vasculature on a large scale. This research aims to develop a novel robust, i.e. with high performance and generalizable, data-driven algorithm for A/V segmentation.
Methods	We created a new digital fundus image (DFI) dataset, named UZF, which contains a total of 240 crowd-sourced A/V segmentations made by fifteen residents and reviewed by a senior oph- thalmologist from the Universitair Ziekenhuis (UZ) Leuven Hospital. To develop LUNet we have adapted the U-Net architecture, redefining the A/V segmentation problem as a multilabel binary segmentation and designed a custom loss function based on a weighted average of the most commonly used function for binary segmentation while taking into account the continuous tubular structure of the blood vessels. We evaluate the generalization performance of LUNet on two external datasets from the United Sates and Paraguay respectively. We compare LUNet’s performances against an open source state-of-the-art (SOTA) algorithms.
Results	LUNet achieved an average dice score of 84.56 on the arterioles and 82.43 on the veins on the UZF dataset and on the external test sets. This was significantly (p<0.5) better than the SOTA benchmark. Conclusion: We introduce LUNet a robust and generalizable deep learning algorithms for A/V segmentation in DFI.
Conclusion	LUNet outperforms existing algorithms to segment the retinal vasculature.
Conflict of interest	No

Authors 1

Last name	VAN EIJGEN
Initials	J
Department	KU Leuven
City	Leuven

Authors 2

Last name	Fhima
Initials	J
Department	Technion
City	Haifa

Authors 3

Last name	Behar
Initials	J
Department	Technion
City	Haifa

Authors 4

Last name	Christinaki
Initials	E
Department	KU Leuven
City	Leuven

Authors 5

Last name	Stalmans
Initials	I
Department	KU Leuven
City	Leuven