Abstract
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.
Highlights
Observed in pathology images because the damaged tissue forms irregular cell structures and undergoes dynamic healing processes[11]
While nuclei segmentation has been widely applied in cancerous cells studies[12,15], its application in other pathology disciplines requires more intricate techniques, because after tissue injury nuclei of existing cell subtypes may morphologically change and new cell types can migrate into the damaged tissue
We demonstrate the utility of Nuquantus in cardiac tissue images taken from animal models of ischemic cardiac injury, known as myocardial infarction (MI)
Summary
Observed in pathology images because the damaged tissue forms irregular cell structures and undergoes dynamic healing processes[11]. While nuclei segmentation has been widely applied in cancerous cells studies[12,15], its application in other pathology disciplines (e.g. acute tissue injury, ischemia, hypoxia) requires more intricate techniques, because after tissue injury nuclei of existing cell subtypes may morphologically change and new cell types can migrate into the damaged tissue. Some of these different cells subtypes may exhibit similar nuclei features, making global nuclei segmentation indistinguishable between their matching cell subpopulations. Nuquantus is a novel supervised machine learning framework that segments, classifies and quantifies nuclei of cells of interest in complicated tissue images. The identification of CMs and CM nuclei becomes further complicated since CMs can morphologically remodel or uncouple from their neighbors, in concurrence with massive infiltration of inflammatory cells[18]
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