LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images

Jonathan Fhima, Jan Van Eijgen, Marie Isaline Billen Moulin-Romsée, Heloïse Brackenier, Hana Kulenovic, Valérie Debeuf, Marie Vangilbergen, Moti Freiman, Ingeborg Stalmans, Joachim A. Behar

Research output: Contribution to journalArticlepeer-review

Abstract

Objective.This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health.Approach.We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation.Main Results.LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators.Significance.The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.

Original languageEnglish
JournalPhysiological Measurement
Volume45
Issue number5
DOIs
StatePublished - 3 May 2024

Keywords

  • Eye vasculature
  • deep learning
  • microvasculature
  • retinal fundus images
  • segmentation

All Science Journal Classification (ASJC) codes

  • Physiology (medical)
  • Biophysics
  • Physiology
  • Biomedical Engineering

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