Fluorescent microscope imaging technologies have developed at a rapid pace in recent years. High-throughput 2D fluorescent imaging platforms are now in wide use and are being applied on a proteome wide scale. Multiple fluorophore 3D imaging of live cells is being used to give detailed localization and subcellular structure information. Further, 2D and 3D video microscopy are giving important insights into the dynamics of protein localization and transport. In parallel with these developments, significant research has gone into developing new methodologies for quantifying and extracting meaning from the imaging data.
Analyzes, processes and visualizes multi-dimensional microscopy images. BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes.
An open source application for the visualization and analysis of 4D biological datasets. Developed by researchers, it is primarily used for the analysis and quantification of 4D live-imaged confocal data. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth.
Facilitates common analysis tasks related to fluorescence imaging. Functionality SIMA includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. A graphical user interface (GUI) has also been developed for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages.
Detects statistically robust phenotypic differences. MiCASA is an unbiased, user-independent method for identifying the phenotypic differences between conditions. It focuses attention on those aspects of the phenotype that have statistical relevance. This package may be used to provide a useful summary of the distribution and interactions between well-known cell types in the thymus. However, MiCASA can be used with any pair of markers.
Aims to reduce labour and to decrease subjectivity in counting individual neighbouring cells. Cell-o-Tape can automatically determine the location of a feature point traditionally estimated visually. It permits users to specify exactly where on the image it operates. This tool allows users to improve the quality of the output using their expert knowledge, whilst performing the main labour-intensive processing in an automated, objective way.
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