Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections
Background. Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis fo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Immunology Research |
| Online Access: | http://dx.doi.org/10.1155/2019/7232781 |
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| author | Nayana Damiani Macedo Aline Rodrigues Buzin Isabela Bastos de Araujo Breno Valentim Nogueira Tadeu Uggere Andrade Denise Coutinho Endringer Dominik Lenz |
| author_facet | Nayana Damiani Macedo Aline Rodrigues Buzin Isabela Bastos de Araujo Breno Valentim Nogueira Tadeu Uggere Andrade Denise Coutinho Endringer Dominik Lenz |
| author_sort | Nayana Damiani Macedo |
| collection | DOAJ |
| description | Background. Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia. Methods. Light microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF+ and VEGF- cells. Results. VEGF was detected and cells were counted with high sensitivity and specificity. Conclusion. With great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach. |
| format | Article |
| id | doaj-art-8107a774963f40d2babbf94af87c224c |
| institution | Kabale University |
| issn | 2314-8861 2314-7156 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Immunology Research |
| spelling | doaj-art-8107a774963f40d2babbf94af87c224c2025-02-03T06:13:12ZengWileyJournal of Immunology Research2314-88612314-71562019-01-01201910.1155/2019/72327817232781Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue SectionsNayana Damiani Macedo0Aline Rodrigues Buzin1Isabela Bastos de Araujo2Breno Valentim Nogueira3Tadeu Uggere Andrade4Denise Coutinho Endringer5Dominik Lenz6University Vila Velha, Pharmaceutical Sciences, Vila Velha, BrazilUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, BrazilDepartment of Morphology, Federal University of Espírito Santo, Vitória, BrazilDepartment of Morphology, Federal University of Espírito Santo, Vitória, BrazilUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, BrazilUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, BrazilUniversity Vila Velha, Pharmaceutical Sciences, Vila Velha, BrazilBackground. Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Reproducible image analysis of biological samples is steadily gaining scientific importance. The aim of the present study was to employ image analysis followed by machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia. Methods. Light microscopy images of renal tissue sections stained for VEGF were analyzed. Subsequently, machine learning classified the cells as VEGF+ and VEGF- cells. Results. VEGF was detected and cells were counted with high sensitivity and specificity. Conclusion. With great clinical, diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach.http://dx.doi.org/10.1155/2019/7232781 |
| spellingShingle | Nayana Damiani Macedo Aline Rodrigues Buzin Isabela Bastos de Araujo Breno Valentim Nogueira Tadeu Uggere Andrade Denise Coutinho Endringer Dominik Lenz Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections Journal of Immunology Research |
| title | Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections |
| title_full | Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections |
| title_fullStr | Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections |
| title_full_unstemmed | Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections |
| title_short | Automated and Reproducible Detection of Vascular Endothelial Growth Factor (VEGF) in Renal Tissue Sections |
| title_sort | automated and reproducible detection of vascular endothelial growth factor vegf in renal tissue sections |
| url | http://dx.doi.org/10.1155/2019/7232781 |
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