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LirotAILogoS.png for retinal images analysis using artificial intelligence is a scholarly resource developed by the AIMLab. and committed to making AI-driven analysis of ophthalmology images widely available, simplifying the integration of AI in the study of ophthalmology and promoting equitable access to AI technology in the field for research and educational purposes.

A set of modules will be released over the next few months as part of our iOS software and this page will be updated accordingly.

Recommended devices: Apple Silicon (M series) chip, iPad with Apple Pencil.

Formats supported: JPG, PNG and TIFF.

Release date: 01-02-2024


LUNet Module

Deep learning for the segmentation of arterioles and venules in high resolution fundus images.

The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distribution of the retinal microvasculature may change with cardiovascular diseases and thus the eyes may be regarded as a window to our hearts. Computerized segmentation of the retinal arterioles and venules (A/V) is essential for automated microvasculature analysis. We developed LUNet, a novel deep learning architecture for high resolution A/V segmentation The LUNet module also provides a user-friendly interface for editing the automated segmentations.

Module features:

  • Segmentation of arterioles (A) and venules (V).

  • User interface for manual correction.

  • Suitable for disk centered DFI only.

This module is released in collaboration with Prof. Ingeborg Stalmans and Dr. Jan Van Eijgen from KU Leuven, Belgium. We also acknowledge the contributions of Dr. Moti Freiman and Motti Koren at the Technion.

When using this module please cite: Fhima, Jonathan, Jan Van Eijgen, Hana Kulenovic, Valérie Debeuf, Marie Vangilbergen, Marie-Isaline Billen, Heloïse Brackenier, Moti Freiman, Ingeborg Stalmans, and Joachim A. Behar. "LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images." arXiv preprint arXiv:2309.05780 (2023).

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