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



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

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.



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.

ANTs: Advanced Normalization Tools


ANTs computes high-dimensional mappings to capture the statistics of brain structure and function.

Image Registration

Diffeomorphisms: SyN, Independent Evaluation: Klein, Murphy, Template Construction (2004)(2010), Similarity Metrics, Multivariate registration, Multiple modality analysis and statistical bias

Image Segmentation

Atropos Multivar-EM Segmentation (link), Multi-atlas methods (link) and JLF, Bias Correction (link), DiReCT cortical thickness (link), DiReCT in chimpanzees


Advanced Normalization Tools

NeuroAnatomy Toolbox


An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons. nat is the core package of a wider suite of neuroanatomy tools introduced at http://jefferislab.github.io.

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VMTK: Vascular Modeling Toolkit


vmtk is a collection of libraries and tools for 3D reconstruction, geometric analysis, mesh generation and surface data analysis for image-based modeling of blood vessels.

vmtk is composed of

  • C++ classes (VTK and ITK -based algorithms)
  • Python classes (high-level functionality - each class is a script)
  • PypeS - Python pipeable scripts, a framework which enables vmtk scripts to interact with each other