A computational framework to annotate complex cellular dynamics. A machine-learning method that combines state-of-the-art classification with hidden Markov modeling for annotation of the progression through morphologically distinct biological states was developed. CellCognition is published as open source software, enabling live-cell imaging-based screening with assays that directly score cellular dynamics.
A free, open-source system designed for flexible, high-throughput cell image analysis. CellProfiler can address a variety of biological questions quantitatively, including standard assays (for example, cell count, size, per-cell protein levels) and complex morphological assays (for example, cell/organelle shape or subcellular patterns of DNA or protein staining).
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).
Enables rapid identification of phenotypically abnormal structures. Data are easily loaded by drag and drop, with support for most commonly used file formats. On loading the registration results, embryos may be overlaid with the t-statistic heatmaps to reveal regions of dysmorphology. We have adopted a hot red/blue colour scheme to be consistent with previously reported results. The heatmap data can be filtered by t statistic value to emphasize regions of different statistical significances. Vector field data can also be loaded into VPV and filtered by magnitude to identify where the most significant deformations have taken place during registration.
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.
It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. The customizability of BIOCAT is expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation.
A user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction.
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.
Allows multiple class classification, elucidating complex phenotypes. Enhanced CellClassifier is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.
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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