The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility—a central process in tissue repair and evolution of disease—subject to various treatments conditions. However processing the resulting data is a laborious task due its high throughput and variability across images.
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).
Evaluates the open image area. TScratch is based on a method which uses an edge-detection algorithm based on the discrete curvelet transform. This tool automates the analysis of wound healing assays performed with a range of cell lines with differing cell morphology. It can be useful for the investigation of a wound healing assay image data set that was analyzed using manual open area quantification.
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.
Analyzes biomedical images acquired by various types of microscopes. MiToBo completely separates the implementation of image processing algorithms from potential user interfaces. MiToBo provides a framework for implementing image analysis algorithms allowing for automatic documentation and automatic user interface generation, and includes the graphical programming editor Grappa for user-friendly design of more complex processing pipelines.
A free, cross-platform, open source software for high throughput time-lapse data processing for live cell imaging. TLA is a graphical tool which enables easy access to high-throughput live cell imaging for every user, regardless of the individual expertise. Beginners can easily process stacks of time-lapse data by loading an appropriate image processing setup and time-lapse evaluation setup. Advanced users can modify existing setups, or create new setups which suit their need and special experimental conditions.
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