A digital pathology tool for quantification of color features in histologic specimens. Issue 1 (24th August 2021)

• Record Type: Journal Article
• Authors: Reschke, Melanie; DiRito, Jenna R.; Stern, David; Day, Wesley; Plebanek, Natalie; Harris, Matthew; Hosgood, Sarah A.; Nicholson, Michael L.; Haakinson, Danielle J.; Zhang, Xuchen; Mehal, Wajahat Z.; Ouyang, Xinshou; Pober, Jordan S.; Saltzman, W. Mark; Tietjen, Gregory T.
• Is Part Of: Bioengineering & translational medicine. Volume 7:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-24
• Subjects: color image analysis -- histology -- human organ research -- immunohistochemistry; Bioengineering -- Periodicals; Drug development -- Periodicals; Drugs -- Testing -- Periodicals; 660.6
• DOI: 10.1002/btm2.10242
• Languages: English
• ISSNs: 2380-6761

Abstract

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.