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PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

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Abstract

Introduction: Blood vessels can be non-invasively visualized from a digital fundus image (DFI). Several studies have shown an association between cardiovascular risk and vascular features obtained from DFI. Recent advances in computer vision and image segmentation enable automatising DFI blood vessel segmentation. There is a need for a resource that can automatically compute digital vasculature biomarkers (VBM) from these segmented DFI. Methods: In this paper, we introduce a Python Vasculature BioMarker toolbox, denoted PVBM. A total of 11 VBMs were implemented. In particular, we introduce new algorithmic methods to estimate tortuosity and branching angles. Using PVBM, and as a proof of usability, we analyze geometric vascular differences between glaucomatous patients and healthy controls. Results: We built a fully automated vasculature biomarker toolbox based on DFI segmentations and provided a proof of usability to characterize the vascular changes in glaucoma. For arterioles and venules, all biomarkers were significant and lower in glaucoma patients compared to healthy controls except for tortuosity, venular singularity length and venular branching angles. Conclusion: We have automated the computation of 11 VBMs from retinal blood vessel segmentation. The PVBM toolbox is made open source under a GNU GPL 3 license and is available on physiozoo.com (following publication).

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Acknowledgment

The research was supported by Grant No ERANET - 2031470 from the Ministry of Health, by the Israel PBC-VATAT and by the Technion Center for Machine Learning and Intelligent Systems (MLIS).

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Authors and Affiliations

  1. Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel

    Jonathan Fhima, Yevgeniy Men, Moti Freiman & Joachim A. Behar

  2. Department of Applied Mathematics Technion-IIT, Haifa, Israel

    Jonathan Fhima

  3. Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium

    Jan Van Eijgen & Ingeborg Stalmans

  4. Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium

    Jan Van Eijgen & Ingeborg Stalmans

  5. The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering, Technion-IIT, Haifa, Israel

    Yevgeniy Men

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Correspondence to Jonathan Fhima .

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  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Fhima, J., Eijgen, J.V., Stalmans, I., Men, Y., Freiman, M., Behar, J.A. (2023). PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13803. Springer, Cham. https://doi.org/10.1007/978-3-031-25066-8_15

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  • DOI: https://doi.org/10.1007/978-3-031-25066-8_15

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