title: Nuquantus: Machine learning software for the characterization and quantification of cell nuclei in complex immunofluorescent tissue images.
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.
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Polina
authors.initials: P
authors.lastname: Gross
authors.affiliation: University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Department of Radiology, Philadelphia, PA, 19104, USA.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Honnorat
authors.affiliation: University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Department of Radiology, Philadelphia, PA, 19104, USA.
authors.forename: Erdem
authors.initials: E
authors.lastname: Varol
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Markus
authors.initials: M
authors.lastname: Wallner
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Danielle M
authors.initials: DM
authors.lastname: Trappanese
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Sharp
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Starosta
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Jason M
authors.initials: JM
authors.lastname: Duran
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Sarah
authors.initials: S
authors.lastname: Koller
authors.affiliation: University of Pennsylvania, Center for Biomedical Image Computing and Analytics, Department of Radiology, Philadelphia, PA, 19104, USA.
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
authors.affiliation: Temple University School of Medicine, Cardiovascular Research Center, Department of Physiology, Philadelphia, PA 19140, USA.
authors.forename: Steven R
authors.initials: SR
authors.lastname: Houser
mesh_terms: D000818:Animals; D002467:Cell Nucleus / Q000378:metabolism*; D049109:Cell Proliferation; D002470:Cell Survival; D005455:Fluorescent Antibody Technique / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D018613:Microscopy, Confocal; D032383:Myocytes, Cardiac / Q000166:cytology* / Q000378:metabolism; D012984:Software