Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology
Authors: Adamo, A., Bruno, A., Menallo, G., Francipane, M.G., Fazzari, M., Pirrone, R., Ardizzone, E., Wagner, W.R. and D’Amore, A.
Journal: Annals of Biomedical Engineering
Volume: 50
Issue: 4
Pages: 387-400
eISSN: 1573-9686
ISSN: 0090-6964
DOI: 10.1007/s10439-022-02923-2
Abstract: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.
https://eprints.bournemouth.ac.uk/36655/
Source: Scopus
Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology.
Authors: Adamo, A., Bruno, A., Menallo, G., Francipane, M.G., Fazzari, M., Pirrone, R., Ardizzone, E., Wagner, W.R. and D'Amore, A.
Journal: Ann Biomed Eng
Volume: 50
Issue: 4
Pages: 387-400
eISSN: 1573-9686
DOI: 10.1007/s10439-022-02923-2
Abstract: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.
https://eprints.bournemouth.ac.uk/36655/
Source: PubMed
Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology
Authors: Adamo, A., Bruno, A., Menallo, G., Francipane, M.G., Fazzari, M., Pirrone, R., Ardizzone, E., Wagner, W.R. and D'Amore, A.
Journal: ANNALS OF BIOMEDICAL ENGINEERING
Volume: 50
Issue: 4
Pages: 387-400
eISSN: 1573-9686
ISSN: 0090-6964
DOI: 10.1007/s10439-022-02923-2
https://eprints.bournemouth.ac.uk/36655/
Source: Web of Science (Lite)
Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology.
Authors: Adamo, A., Bruno, A., Menallo, G., Francipane, M.G., Fazzari, M., Pirrone, R., Ardizzone, E., Wagner, W.R. and D'Amore, A.
Journal: Annals of biomedical engineering
Volume: 50
Issue: 4
Pages: 387-400
eISSN: 1573-9686
ISSN: 0090-6964
DOI: 10.1007/s10439-022-02923-2
Abstract: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.
https://eprints.bournemouth.ac.uk/36655/
Source: Europe PubMed Central
Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology.
Authors: Adamo, A., Bruno, A., Menallo, G., Francipane, M.G., Fazzari, M., Pirrone, R., Ardizzone, E., Wagner, W.R. and D'Amore, A.
Journal: Annals of Biomedical Engineering
Volume: 50
Pages: 387-400
ISSN: 0090-6964
Abstract: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.
https://eprints.bournemouth.ac.uk/36655/
Source: BURO EPrints