Abstract
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.
Highlights
The manual analysis of tissue sections, such as the analysis performed for pathological diagnosis, requires an analyst with substantial knowledge and experience [1, 2]
The aim of the present study was to employ image analysis and subsequent machine learning to identify vascular endothelial growth factor (VEGF) in kidney tissue that had been subjected to hypoxia
The staining was visualized with the peroxidase reaction with 3,3′-diaminobenzidine tetrahydrochloride (DAB; Sigma Chemical Co., USA)
Summary
The manual analysis of tissue sections, such as the analysis performed for pathological diagnosis, requires an analyst with substantial knowledge and experience [1, 2]. In most biological tissue analyses, e.g., immunohistochemistry, cells are counted manually [4]. Manual tissue analysis and cell counting are considered subjective, tedious, and time consuming, resulting in intra-analyst variance [4,5,6,7,8]. The importance of reproducible image analysis of biological samples, i.e., an automated process for identifying objects of interest and performing a subsequent quantitative per-object analysis, is steadily being recognized by the scientific community [11, 12]. Manual analysis of tissue sections, such as for pathological diagnosis, requires an analyst with substantial knowledge and experience. Diagnostic, and research potential, automatic image analysis offers a new quantitative capability, thereby adding numerical information to a mostly qualitative diagnostic approach
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