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

Jonathan Fhima; Jan Van Eijgen; Ingeborg Stalmans; Yevgeniy Men; Moti Freiman; Joachim A. Behar

doi:10.1007/978-3-031-25066-8_15

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

Jonathan Fhima, Jan Van Eijgen, Ingeborg Stalmans, Yevgeniy Men, Moti Freiman, Joachim A. Behar

Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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).

Original language	English
Title of host publication	Computer Vision – ECCV 2022 Workshops, Proceedings
Editors	Leonid Karlinsky, Tomer Michaeli, Ko Nishino
Pages	296-312
Number of pages	17
DOIs	https://doi.org/10.1007/978-3-031-25066-8_15
State	Published - 2023
Event	17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel Duration: 23 Oct 2022 → 27 Oct 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13803 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th European Conference on Computer Vision, ECCV 2022
Country/Territory	Israel
City	Tel Aviv
Period	23/10/22 → 27/10/22

Keywords

Digital fundus images
Digital vascular biomarkers
Glaucoma
Retinal vasculature

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-25066-8_15

Cite this

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 L. Karlinsky, T. Michaeli, & K. Nishino (Eds.), Computer Vision – ECCV 2022 Workshops, Proceedings (pp. 296-312). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13803 LNCS). https://doi.org/10.1007/978-3-031-25066-8_15

Fhima, Jonathan ; Eijgen, Jan Van ; Stalmans, Ingeborg et al. / PVBM : A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation. Computer Vision – ECCV 2022 Workshops, Proceedings. editor / Leonid Karlinsky ; Tomer Michaeli ; Ko Nishino. 2023. pp. 296-312 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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Fhima, J, Eijgen, JV, Stalmans, I, Men, Y, Freiman, M & Behar, JA 2023, PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation. in L Karlinsky, T Michaeli & K Nishino (eds), Computer Vision – ECCV 2022 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13803 LNCS, pp. 296-312, 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv, Israel, 23/10/22. https://doi.org/10.1007/978-3-031-25066-8_15

PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation. / Fhima, Jonathan; Eijgen, Jan Van; Stalmans, Ingeborg et al.
Computer Vision – ECCV 2022 Workshops, Proceedings. ed. / Leonid Karlinsky; Tomer Michaeli; Ko Nishino. 2023. p. 296-312 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13803 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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. 2023. p. 296-312. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25066-8_15