A collection is a software that encapsulate a set of bioimage components and/or workflows.



Elastix is a toolbox for rigid and nonrigid registration of (medical) images.

Elastix is based on the ITK library, and provides additional algorithms for image registration. 

The software can be run as a single-line command, making it easy to include in larger scripts or workflows. The user needs to edit a configuration file that contains all relevant parameters for registration: transformation model, metric used to comapre images, optimization algorithm, mutliscale pyramidal representation of images...

Nowadays elastix is accompanied by SimpleElastix, making it available in other languages like C++, Python, Java, R, Ruby, C# and Lua.

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Cancer Imaging Phenomics Toolkit (CaPTk)


CaPTk is a software platform for analysis of radiographic cancer images, currently focusing on brain, breast, and lung cancer. CaPTk integrates advanced, validated tools performing various aspects of medical image analysis, that have been developed in the context of active clinical research studies and collaborations toward addressing real clinical needs. With emphasis given in its use as a very lightweight and efficient viewer, and with no prerequisites for substantial computational background, CaPTk aims to facilitate the swift translation of advanced computational algorithms into routine clinical quantification, analysis, decision making, and reporting workflow. Its long-term goal is providing widely used technology that leverages the value of advanced imaging analytics in cancer prediction, diagnosis and prognosis, as well as in better understanding the biological mechanisms of cancer development.




NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

  • Get started with established pre-trained networks using built-in tools;
  • Adapt existing networks to your imaging data;
  • Quickly build new solutions to your own image analysis problems.

Galaxy Image Analysis Tools


Image analysis tools to be used within Galaxy

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Galaxy imaging workflow

Galaxy Workbench for Image Analysis


Galaxy instance with tools for Image analyses shipped in a Docker container.

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Orbit Image Analysis is a free open source software with the focus to quantify big images like whole slide scans.

It can connect to image servers, e.g. Omero.
Analysis can be done on your local computer or via scaleout functionality in a distrubuted computing environment like a Spark cluster.

Sophisticated image analysis algorithms incl. tissue quantification using machine learning, object segmentation and classification are build in. In addition a versatile API allows you to enhance Orbit and to run your own scripts.




Computer-assisted Evaluation of Myelin formation (CEM) is a collection designed to automate myelin quantification. It requires use input to choose the best threshold values. The myelin is calculated as an overlap between neuronal signal and oligodendrocyte signal. Results are given as pixel counts and percents.

CEM runs as an imageJ plugin with an optional Matlab extension to remove cell bodies. More details are published at Kerman et al. 2015 Development. Supplemental Material includes a detailed user manual and the download link.




MaMuT is an end user plugin that combines the BigDataViewer and TrackMate to provide an application that allow browsing, annotating and curating annotations for large image data.



"An open source machine learning framework for everyone "

TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

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The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.