  Determination of fundamental mechanisms of disease often hinges on
histopathology visualization and quantitative image analysis. Currently, the
analysis of multi-channel fluorescence tissue images is primarily achieved by
manual measurements of tissue cellular content and sub-cellular compartments.
Since the current manual methodology for image analysis is a tedious and
subjective approach, there is clearly a need for an automated analytical
technique to process large-scale image datasets. Here, we introduce Nuquantus
(Nuclei quantification utility software) - a novel machine learning-based
analytical method, which identifies, quantifies and classifies nuclei based on
cells of interest in composite fluorescent tissue images, in which cell borders
are not visible. Nuquantus is an adaptive framework that learns the
morphological attributes of intact tissue in the presence of anatomical
variability and pathological processes. Nuquantus allowed us to robustly
perform quantitative image analysis on remodeling cardiac tissue after
myocardial infarction. Nuquantus reliably classifies cardiomyocyte versus
non-cardiomyocyte nuclei and detects cell proliferation, as well as cell death
in different cell classes. Broadly, Nuquantus provides innovative computerized
methodology to analyze complex tissue images that significantly facilitates
image analysis and minimizes human bias.
