| Autor: |
Melanie Reschke, Jenna R. DiRito, David Stern, Wesley Day, Natalie Plebanek, Matthew Harris, Sarah A. Hosgood, Michael L. Nicholson, Danielle J. Haakinson, Xuchen Zhang, Wajahat Z. Mehal, Xinshou Ouyang, Jordan S. Pober, W. Mark Saltzman, Gregory T. Tietjen |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Bioengineering & Translational Medicine, Vol 7, Iss 1, Pp n/a-n/a (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2380-6761 |
| DOI: |
10.1002/btm2.10242 |
| Popis: |
Abstract In preclinical research, histological analysis of tissue samples is often limited to qualitative or semiquantitative scoring assessments. The reliability of this analysis can be impaired by the subjectivity of these approaches, even when read by experienced pathologists. Furthermore, the laborious nature of manual image assessments often leads to the analysis being restricted to a relatively small number of images that may not accurately represent the whole sample. Thus, there is a clear need for automated image analysis tools that can provide robust and rapid quantification of histologic samples from paraffin‐embedded or cryopreserved tissues. To address this need, we have developed a color image analysis algorithm (DigiPath) to quantify distinct color features in histologic sections. We demonstrate the utility of this tool across multiple types of tissue samples and pathologic features, and compare results from our program to other quantitative approaches such as color thresholding and hand tracing. We believe this tool will enable more thorough and reliable characterization of histological samples to facilitate better rigor and reproducibility in tissue‐based analyses. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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