Automated quantification and statistical assessment of proliferating cardiomyocyte rates in embryonic hearts

Abstract

The use of digital image analysis and count regression models contributes to the reproducibility and rigor of histological studies in cardiovascular research. The use of formalized computer-based quantification strategies of histological images essentially removes potential researcher bias, allows for higher analysis throughput, and enables easy sharing of formalized quantification tools, contributing to research transparency, and data transferability. Moreover, the use of count regression models rather than ratios in statistical analysis of cell population data incorporates the extent of sampling into analysis and acknowledges the non-Gaussian nature of count distributions. Using quantification of proliferating cardiomyocytes in embryonic murine hearts as an example, we describe how these improvements can be implemented using open-source artificial intelligence-based image analysis tools and novel count regression models to efficiently analyze real-life data.