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Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology.

Adamo, A., Bruno, A., Menallo, G., Francipane, M.G., Fazzari, M., Pirrone, R., Ardizzone, E., Wagner, W.R. and D'Amore, A., 2022. Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology. Annals of Biomedical Engineering, 50, 387-400.

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Adamo2022_Article_BloodVesselDetectionAlgorithmF.pdf - Published Version
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DOI: 10.1007/s10439-022-02923-2


Immunohistochemistry for vascular network analysis plays a fundamental role in basic science, translational research and clinical practice. However, identifying vascularization in histological tissue images is time consuming and markedly depends on the operator's experience. In this study, we present "blood vessel detection-BVD", an automatic algorithm for quantitative analysis of blood vessels in immunohistochemical images. BVD is based on extraction and analysis of low-level image features and spatial filtering techniques, which do not require a training phase. BVD algorithm performance was comparatively evaluated on histological sections from three different in vivo experiments. Collectively, 173 independent images were analyzed, and the algorithm's results were compared to those obtained by human operators. The developed BVD algorithm proved to be a robust and versatile tool, being able to quantify number, area, and spatial distribution of blood vessels within all three considered histologic datasets. BVD is provided as an open-source application working on different operating systems. BVD is supported by a user-friendly graphical interface designed to facilitate large-scale analysis.

Item Type:Article
Uncontrolled Keywords:Angiogenesis ; Automated image analysis ; Biomaterials host response ; Blood vessel formation ; Blood vessel morphology ; Quantitative histology ; Quantitative immunohistochemistry ; Vascularization
Group:Faculty of Science & Technology
ID Code:36655
Deposited By: Symplectic RT2
Deposited On:22 Feb 2022 15:36
Last Modified:18 May 2022 15:37


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