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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.


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).

KiT / Kinetochore Tracking

An easy-to-use, open-source software package for tracking kinetochores from live-cell fluorescent movies. KiT supports 2D, 3D and multi-colour movies, quantification of fluorescence, integrated deconvolution, parallel execution and multiple algorithms for particle localization. KiT includes a user-friendly GUI (Graphical User Interface) for selecting ROIs (Regions Of Interest; to select cells and exclude spurious background fluorescence), parameter configuration and execution of KiT. Tracking may be executed from within the GUI or later. The GUI allows selection of particle detection algorithms per channel and modification of the most commonly used options. KiT also includes a GUI for post-tracking processing, enabling basic diagnostics and track quantification, such as kinetochore speeds and autocorrelation. The GUI collates data from each tracked ROI and saves a .mat file for later user-specific processing.


The performance of the single particle tracking (SPT) nearest-neighbor algorithm is determined by parameters that need to be set according to the characteristics of the time series under study. Inhomogeneous systems, where these characteristics fluctuate spatially, are poorly tracked when parameters are set globally. adaptiveSPT is a SPT approach that adapts the well-known nearest-neighbor tracking algorithm to the local density of particles to overcome the problems of inhomogeneity.

MosaicIA / Mosaic Interaction Analysis

Allows spatial point pattern and interaction analysis. MosaicIA provides standardized ways to (1) correct for the influence of the distribution of points within one set onto the distance distribution to another set; (2) infer parameters of the interaction potential, such as the strength and length scale of the interaction; (3) perform statistical hypothesis tests for the presence of an interaction. It has been tested on both synthetic and real-world data.

Amira 3D Software for Life Sciences

Allows users to visualize, manipulate, and understand data from imaging modalities such as computed tomography, microscopy or Magnetic resonance imaging (MRI). Amira 3D Software for Life Sciences provides features to import and process 2D and 3D images data, visualization techniques and tools for visual analysis. Users can also create and share presentations. The base product can be customized by adding functional extensions to fit special needs in different application areas.


It is based on the MultiTracker plugin by Jeffrey Kuhn which is based on the Object tracker plugin by Wayne Rasband. In contrast to the Multitracker plugin, the number of objects may vary between successive frames (objects may appear or disappear). Mtrack2 will identify the objects in each frame, and then determine which objects in successive frames are closest together. If these are within a user-defined distance (the maximum velocity of the objects) they are assembled into tracks. When multiple objects are within the distance determined by the maximum velocity, the closest object is selected and the object is flagged in the output.


A simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode.