Fluorescent immunophenotyping uses fluorescently-conjugated antibodies to identify, characterize and quantify distinct subpopulations of cells within heterogeneous single-cell populations, either in the context of tissue (using fluorescence and imaging microscopy) or in a single-cell suspension (using multiparameter imaging microscopy, imaging cytometry, and/or flow cytometry).
A software for biological image-based classification, data exploration, and visualization designed for biologists and data scientists. CellProfiler Analyst builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).
Combines state-of-the-art automated neuron tracing and machine learning-enabled neuron classification tools. Aivia provides methods for analyzing time-lapse images. It covers a wide range of applications such as cell/nuclei counting, cell/nuclei tracking, 3D neuron detection and analysis, machine learning cell classification, particle tracking, wound healing and calcium oscillation tracking. Aivia also comes with editing tools to help get even better results.
Permits investigation on live cells. AcuityXpress addresses the needs of high-content data analysis at an enterprise level. It offers functions to construct advanced database queries and to compare multiple experimental conditions side-by-side. This tool can build both potency measurements with over 30 predefined functions or custom functions allowing dose response investigation. It creates interactive connections between images and analysis results.
Allows user to explore high-content microscopy images. PhenoRipper assists users in comparison of images and based on similarity of image phenotypes. It uses cluster analysis to identify superblock types, representing the most common block type co-occurrence patterns. It profiles each image by the frequency of occurrence of superblock types.
Represents a generic novelty detection and deep learning framework which can enables sensitive and accurate cellular phenotype detection. CellCognition Explorer serves for integrated data analysis from raw images to phenotype scores and consists of two programs: (1) the principal CellCognition Explorer permits interactive data visualization tools and the possibility to perform versatile analysis workflows, (2) and the CellCognition Deep Learning Module which is a separate program for graphics processing unit accelerated high-performance computing of deep learning features.
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