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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.
Allows easy online sharing and interactive visualisation of large screen datasets, facilitating their dissemination and further analysis, and enhancing their impact. The user interface allows the members of the community without any computational knowledge to extract meaningful information from the data. The web interface can be used for querying the data and the results are visualized as plots (e.g. scatter plot, histogram) in real-time. Mineotaur is based on a novel data model allowing the visualization and analysis of extremely large amounts of data.
MotIW / Motility study Integrated Workflow
A generic workflow for the study of single cell motility in high-throughput time-lapse screening data. MotIW is composed of cell tracking, cell trajectory mapping to an original feature space and hit detection according to a new statistical procedure. We show that this workflow is scalable and demonstrates its power by application to simulated data, as well as large-scale live cell imaging data. This application enables the identification of an ontology of cell motility patterns in a fully unsupervised manner.
LSDCAS / Large Scale Digital Cell Analysis System
An extensible, automated system for high throughput screening and non-perturbing optical single cell analysis. The analytical capabilities of LSDCAS include automatic cell motility analysis, and the detection and analysis of cell events such as normal and abnormal cell division and cell death. LSDCAS also provides automatic assays for cell proliferation and wound healing. The LSDCAS technology is current being developed as a tool for cancer drug discovery.
IEV / Interactive Embryo Viewer
Allows users to directly compare wild-type and mutant embryos in a dynamic, intuitive manner. With IEV, users can navigate the volumes in synchrony by using the sliders, mouse wheel or crosshair tool (shift key while moving the mouse). The settings panel provides options to toggle viewports on/off, configure the scale bar, zoom in/out, create bookmarks and switch modality. Each viewer has its own drop-down box listing the embryos available to view, an image contrast slider, and the ability to link/unlink individual viewports.
HARP / Harwell Automated Reconstruction Processor
An open-source, cross-platform application. HARP is designed to operate on 3D image data represented as two-dimensional (2D) slices in a variety of formats (tiff, bmp, jpg, etc.). Although it was primarily designed for tomographic images of embryos, it can in practice be used for other imaging modalities, specimen types and fields of study. HARP provides options to automatically crop image volumes to remove any undesirable empty space around the specimens.
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.
TRACMIT / TRACking and analyzing cells on micropatterns through MITosis
Allows image analysis. TRACMIT permits to automate the chromatin feature extraction of mitotic cells grown on micropatterns. It allows screening of live imaging data sets for novel candidates regulating mitotic processes, such as spindle positioning. The tool can select micropatterns that contain exactly one cell, while incorporating pre-processing, tracking, data filtering and visual validation. It offers a way to monitor and quantify cell division 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.
A high throughput method to automatically detect the transition of a cell cluster from two to three cells in thousands of videos. livespin performs a robust implicit tracking of cells even when they are packed, overlap or are not clearly distinguishable. The approach is based on a robust fitting of two-dimensional Gaussian mixture models with two and three components on each frame of the video. livespin is composed of four steps described in this section. The first step consists in localizing the fibronectin patterns and cropping the whole video at those locations to obtain individual cluster sequences, the second step consists in fitting 2- and 3-components Gaussian mixture model (GMM) onto each frame of each video sequence and the third step consists in the identification of the first frame containing three cells (the transition from 2 cells to 3 cells) using the fitting error difference and other features computed from the GMM parameters. The final step consists in the computation of the angle of division in the identified frame.
MORE software analysis / morpho-rheological software analysis
Allows high-throughput single cell morpho-rheological (MORE) characterization of all major blood cells in continuous flow. MORE software analysis allows individual blood cell mechanics to be studied in a range of human diseases. Label-free, disease-specific MORE blood signatures are a novel resource for generating hypotheses about the underlying molecular mechanisms. In addition, MORE software analysis has the potential to become a standard approach in flow cytometry with many applications in biology, biophysics, biotechnology and medicine.
Blood Analysis App
Detects and concentrates estimation of various bio-markers in blood sample images. Blood Analysis App is an Android application which allows the user to take images of the blood samples in a set format. The application segments the image to detect the regions of interest, calculates the intensity of each individual blob after noise removal, fits a linear curve through the intensity and known concentration data and estimates the concentrations of the unknown samples from the standard curve, which will quantify the various molecules present in the sample.
Analyzes images from almost any digital fluorescent microscope or high-content screening device. CyteSeer is an automated cell image analysis software that provides powerful data analysis, gating and statistics tools for sets of cells, wells, plates and slides. It permits to (i) create and share custom cell image analysis algorithms with other CyteSeer users, (ii) browse easily through files and quickly search for measurements and (iii) support luminous field and fluorescence image sets.
Acapella High Content Imaging and Analysis Software
Provides rapid online image data processing and thoroughly flexible analysis for all high content cellular applications, including multi-parametric multiplex assays. It’s the driving force behind the Opera® High Content Screening System and enables you to turn images into statistically relevant results and a greater understanding that can accelerate disease research and lead discovery. Acapella® software is optimized for high speed online image analysis and for processing large data sets.
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