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PhenoPlot
Allows representation of multidimensional cellular measurements. PhenoPlot is a toolbox that permits visualization of up to 21 variables. It may be useful for determining the morphology of breast cancer cell lines or for understanding and interpreting multidimensional cellular imaging data. To assist users, this tool employs many visual elements such as differently sized, coloured and structured objects. It provides effective and intuitive pictorial representations of cellular phenotypes.
EBImage
Provides general purpose functionality for reading, writing, processing and analysis of images. In the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. It uses ImageMagick to read and save images, and supports more than 80 image formats, including JPEG, TIFF, TGA, GIF and PNG. EBImage also supports standard geometric transformations such as rotation, reflection, cropping, translation and resizing. Classical image processing tools are available: linear filtering, morphological erosion and dilation, fast distance map computation, contour delineation and area filling.
CellCognition Explorer
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.
ACC / Advanced Cell Classifier
Offers access to a variety of advanced machine learning methods and provides an accurate high-content screen analysis. Advanced Cell Classifier (ACC) contains two features: (1) it is not limited to a specific classifier, (2) can detect even very small phenotype changes, allowing to identify not only the main but also subphenotypes. Its system permits to work with a graphical user interface (GUI) to create training database for classification, an immediate image classification, a plate browser, a report generation, and a numerous classification algorithms.
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