Aims to analyze the dynamics of macromolecular assemblies with high spatial and temporal resolution. QFSM allows users to study spatial and temporal relations between the formation, turnover, and mechanical outputs of the filament network. It assists in identifying and tracking speckles and utilizes their location, appearance, and disappearance to derive network flows and assembly/disassembly maps.
Provides tools for learning generative models of cell organization directly from images, storing and retrieving those models in XML files and synthesizing cell images (or other representations) from one or more models.
Allows quantification of clathrin-coated pit dynamics from fluorescence time-lapse data. cmeAnalysis provides functionalities including: (1) sensitive detection, (2) tracking (based on u-track), (3) master/slave detection for multi-channel data, (4) intensity-based classification of coated structures, and (5) lifetime analysis. It also contains a graphical user interface (GUI) for inspection of analysis results from individual movies.
Performs 2D immunofluorescence images to pinpoint and number synaptic protein puncta. SynPAnal provides an application that intends to partially automate processing tasks of files generated by confocal laser scanning microscopy. The application focuses on the quantification of puncta attributes but can also be applied for basic fluorescent intensity measuring as well as for basic morphometric analysis of neurons.
Computational methods were developed to automatically analyze the images created by the University of California, San Francisco (UCSF) yeast GFP fusion localization project. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins.
Allows analysis of a range of parameters measured in 3D cell culture based on 2D images. PCaAnalyser is an automated image-analysis based software developed as an ImageJ plugin. The software enables high-throughput analysis of images acquired from cells grown in a 3D matrix. It is able to reproducibly analyze immuno-staining of different markers known to be involved in cancer progression including CXCR4, α6 and β1 integrin subunits.
A cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. We evaluate S-PSorter on a collection of 1,636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that S-PSorter achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method.