Comparison between two programs for image analysis, machine learning and subsequent classification

Abstract

In the early 1950s, flow cytometry was developed as the first method for automated quantitative cellular analysis. In the early 1990s, the first equipment for image cytometry (laser scanning cytometry, LSC) became commercially available. As flow cytometry was considered the gold standard, various studies found that the results of flow cytometry and LSC generated comparable results. One of the first programs for image analysis that included morphological parameters was ImageJ, published in 1997. One of the newer programs for image analysis that is not limited to fluorescence images is the free software CellProfiler. In 2008, the same group published a new software, CellProfiler Analyst. One part of CellProfiler Analyst is a supervised machine-learning-based classifier that allows users to conduct imaging-based diagnoses, e.g., cellular diagnosis based on morphology. Another relatively new, free software for image analysis is QuPath. The aim of the present study was to compare two free programs for conducting image analysis, CellProfiler and QuPath, and the subsequent classification based on machine learning. For this study, images of renal tissue were analyzed, and the identified objects were classified. The same images were loaded in both software programs. Advanced statistical analysis was used to compare the two methods. The Bland-Altman assay showed that all of the differences were within the mean ± 1.96 * standard deviation, i.e., the differences are normally distributed, and the software programs are comparable. For the analyzed samples (renal tissue stained with HIF and TUNEL), the use of QuPath was easier because it offers image analysis without a previous processing of the images (e.g., conversion to grayscale, inverted intensities) and an unsupervised machine learning process.

Keywords: Cellular diagnosis; Image cytometry; Machine learning.