Lirot.ai for retinal images analysis using artificial intelligence.
Lirot.ai 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.
The fundus image allows for the visualization of numerous vascular features, notably the arterioles (small arteries) and venules (small veins). Advanced image processing and machine learning techniques now enable the extraction of arterioles and venules from fundus images, a process known as A/V segmentation. By isolating these vessels, we can examine their morphology and distribution in greater detail, revealing subtle changes that might otherwise go unnoticed. From this A/V segmentation, we can compute vasculature biomarkers, which are quantifiable indicators of biological states or conditions. By analyzing these vasculature biomarkers, healthcare professionals can gain deeper insights into a patient’s ocular health and potentially detect early signs of disease.
Module features:
Digital vasculature biomarkers engineering (18 features).
Including a set of 12 biomarkers computed for arterioles (A) or venules (V).
Defining a region of interest.
Exporting digital vasculature biomarkers for subsequent analysis.
Project page and app: https://www.aimlab-technion.com/lirot-ai
Association publications:
PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation. Fhima J, Eijgen JV, Stalmans I, Men Y, Freiman M, Behar JA. PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation. In Karlinsky L, Michaeli T, Nishino K, editors, Computer Vision – ECCV 2022 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 296-312. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). [DOI] [Link to publication in Scopus]
Computerized analysis of the eye vasculature in a mass dataset of digital fundus images: the example of age, sex and primary open-angle glaucoma. Fhima J, Eijgen JV, Reiner-Benaim A, Beeckmans L, Abramovich O, Stalmans I, Behar JA. medRxiv (2024): 2024-07. [DOI]
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