Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology
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 presen...
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| Veröffentlicht in: | Annals of biomedical engineering 2022-04, Vol.50 (4), p.387-400 |
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| creator | Adamo, A. Bruno, A. Menallo, G. Francipane, M. G. Fazzari, M. Pirrone, R. Ardizzone, E. Wagner, W. R. D’Amore, A. |
| description | 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. |
| doi_str_mv | 10.1007/s10439-022-02923-2 |
| format | Article |
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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.</description><identifier>ISSN: 0090-6964</identifier><identifier>EISSN: 1573-9686</identifier><identifier>DOI: 10.1007/s10439-022-02923-2</identifier><identifier>PMID: 35171393</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Biochemistry ; Biological and Medical Physics ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Biophysics ; Blood vessels ; Classical Mechanics ; Feature extraction ; Histology ; Image filters ; Image processing ; Immunohistochemistry ; Network analysis ; Original ; Original Article ; Quantitative analysis ; Spatial distribution ; Spatial filtering ; Tissue engineering ; Vascularization</subject><ispartof>Annals of biomedical engineering, 2022-04, Vol.50 (4), p.387-400</ispartof><rights>The Author(s) 2022</rights><rights>2022. The Author(s).</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-b2c01d2182312ffa3d08885ae42ac991f64563b29ca0cdce2316cce139a03aeb3</citedby><cites>FETCH-LOGICAL-c474t-b2c01d2182312ffa3d08885ae42ac991f64563b29ca0cdce2316cce139a03aeb3</cites><orcidid>0000-0002-3151-3170 ; 0000-0002-6908-6071</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10439-022-02923-2$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10439-022-02923-2$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35171393$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Adamo, A.</creatorcontrib><creatorcontrib>Bruno, A.</creatorcontrib><creatorcontrib>Menallo, G.</creatorcontrib><creatorcontrib>Francipane, M. G.</creatorcontrib><creatorcontrib>Fazzari, M.</creatorcontrib><creatorcontrib>Pirrone, R.</creatorcontrib><creatorcontrib>Ardizzone, E.</creatorcontrib><creatorcontrib>Wagner, W. R.</creatorcontrib><creatorcontrib>D’Amore, A.</creatorcontrib><title>Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology</title><title>Annals of biomedical engineering</title><addtitle>Ann Biomed Eng</addtitle><addtitle>Ann Biomed Eng</addtitle><description>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.</description><subject>Algorithms</subject><subject>Biochemistry</subject><subject>Biological and Medical Physics</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Biophysics</subject><subject>Blood vessels</subject><subject>Classical Mechanics</subject><subject>Feature extraction</subject><subject>Histology</subject><subject>Image filters</subject><subject>Image processing</subject><subject>Immunohistochemistry</subject><subject>Network analysis</subject><subject>Original</subject><subject>Original Article</subject><subject>Quantitative analysis</subject><subject>Spatial distribution</subject><subject>Spatial filtering</subject><subject>Tissue engineering</subject><subject>Vascularization</subject><issn>0090-6964</issn><issn>1573-9686</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kUFv1DAQhS0EokvhD3BAlrhwCYztxLEvSG0ptFIlBCpcLa8zSV1l7cV2KvXf43ZLKT1wsObgb97Mm0fIawbvGUD_ITNohW6A8_o0Fw1_Qlas60WjpZJPyQpAQyO1bPfIi5wvARhTontO9kTHeia0WJHvh3OMA_2JOeNMP2FBV3wM9GCeYvLlYkPHmOi5z3lBehwmHxCTDxO1YaDfFhuKL7b4K6QnPpc4x-n6JXk22jnjq7u6T358Pj4_OmnOvn45PTo4a1zbt6VZcwds4Exxwfg4WjGAUqqz2HLrtGajbDsp1lw7C25wWDHpHNa1LQiLa7FPPu50t8t6g5UIJdnZbJPf2HRtovXm35_gL8wUr4zS1T3oKvDuTiDFXwvmYjY-O5xnGzAu2XDJtVCt0n1F3z5CL-OSQrVXKaFY34FUleI7yqWYc8LxfhkG5iYys4vM1MjMbWSG16Y3D23ct_zJqAJiB-TtzeUx_Z39H9nfZhCivA</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Adamo, A.</creator><creator>Bruno, A.</creator><creator>Menallo, G.</creator><creator>Francipane, M. 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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.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>35171393</pmid><doi>10.1007/s10439-022-02923-2</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-3151-3170</orcidid><orcidid>https://orcid.org/0000-0002-6908-6071</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Biochemistry Biological and Medical Physics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Blood vessels Classical Mechanics Feature extraction Histology Image filters Image processing Immunohistochemistry Network analysis Original Original Article Quantitative analysis Spatial distribution Spatial filtering Tissue engineering Vascularization |
| title | Blood Vessel Detection Algorithm for Tissue Engineering and Quantitative Histology |
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