Identifies cells of interest on imaging of large cell numbers in quantitative microscopy. Micropilot can automatically process complex fluorescence microscopy-based imaging assays. Users can train the software to detect objects in a fast low-resolution prescanning mode. This software is able to execute more complex imaging assays on selected object positions by following an online reconfiguration of the microscope system.
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.
Detects patch-based carcinoma in confocal laser endomicroscopy (CLE) images. This patch probability fusion method that can provide additional real-time information about the suspicious lesion, supportive to the clinical examination. The software can serve as an additional tool supporting the biopsy and the following histopathological examination. It was applied on CLE images of oral squamous cell carcinoma (OSCC).
Supports local analysis and is able to split a single trajectory into segments with different motion types. TraJClassifier is a Fiji plugin providing a practical and device-independent method to classify and segment particle trajectories into four main motion types. It loads trajectories from TrackMate (exported via the action "Export trajectories"), characterizes them using TraJ and classifies them into normal diffusion, subdiffusion, confined diffusion and directed/active motion by a random forest approach (through Renjin).
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.
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