KNOSSOS visualizes large 3D (up to multiple terabyte) volume electron microscopic (e.g. Serial Block-Face EM) datasets by displaying slices. It supports skeleton and volume-based annotation modes, which can be extended by plugins written in Python.

It is developed at the Max Planck Institute for Medical Research in Heidelberg, Germany, for Windows, GNU/Linux and OS X. A similar, web-based implementation is being developed at webknossos.info.

Take a look at KNOSSOS’ features to learn more about it:

3D Visualization

3D visualization of image datasets is done by displaying a 2D representation of each side, and allowing you to navigate through these image planes. By dynamically loading only data from the surrounding of the current location, seamless navigation is not limited to datasets that fit into the available RAM but also works with much larger datasets stored in KNOSSOS’ special format on disk.

KNOSSOS 3D Visualization Example

3D Annotation

KNOSSOS supports two annotation methods—skeletonization as well as 3D segmentation for volume reconstruction. Skeletonization is done by placing and connecting nodes, while KNOSSOS’ segmentation mode allows manual processing of pre-segmented data, and creation of new segmentations from scratch.

These features are already being used at Max Planck Institute for Medical Research (among others), where mice retina was successfully reconstructed.

KNOSSOS Segmentation Example


You can enhance KNOSSOS’ annotation features by writing Python plugins, and we also provide a Python script that helps you to convert your existing image data into a KNOSSOS-readable format.

Open Source & Cross-Platform

KNOSSOS is developed using the Qt5 toolkit, and available on all major platforms. You can help the development of KNOSSOS by submitting bugs and other suggestions at Github’s issue tracker or by contacting us directly.


Date of Publication Title of Publication DOI Link
March 27, 2017 EM connectomics reveals axonal target variation in a sequence-generating network http://dx.doi.org/10.7554/eLife.24364
February 27, 2017 Automated synaptic connectivity inference for volume electron microscopy http://doi.org/10.1038/nmeth.4206
February 08, 2017 Volume Electron Microscopic Analyses in the Larval Zebrafish http://doi.org/10.11588/heidok.00022556
February 06, 2017 When complex neuronal structures may not matter http://dx.doi.org/10.7554/eLife.23508
November 08, 2016 3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons https://dx.doi.org/10.1038%2Fsdata.2016.100
July 26, 2016 Connectivity map of bipolar cells and photoreceptors in the mouse retina https://doi.org/10.1101/065722
July 07, 2016 Species-specific wiring for direction selectivity in the mammalian retina http://dx.doi.org/10.1038%2Fnature18609
August 08, 2013 Connectomic reconstruction of the inner plexiform layer in the mouse retina http://doi.org/10.1038/nature12346
July 10, 2011 High-accuracy neurite reconstruction for high-throughput neuroanatomy http://doi.org/10.1038/nn.2868
March 09, 2011 Wiring specificity in the direction-selectivity circuit of the retina http://doi.org/10.1038/nature09818

Your paper? If you think KNOSSOS might be useful for your research, feel free to contact us. We are happy to exchange ideas and to provide assistance!

Getting started

A special image format is required to use KNOSSOS. You have the choice to either try our example datasets, or learn how to create your own.

The built-in datasets are called ek2006 and ek0563.

An external dataset can be loaded into KNOSSOS by selecting its .conf file in FileChoose Dataset..., and clicking on Use.

Own Datasets

If you have your own image datasets, they will probably need to be converted into KNOSSOS’ format. Take a look at our documentation to learn how to do so.

Offline Datasets

These are offline datasets that already contain all pre-formatted images:

Help & Contribute

Bug Reports & Feature Requests

KNOSSOS is being actively maintained to provide a useful tool to the study of connectomics. If you miss a feature that would make KNOSSOS more useful for your research, please tell us on our issue tracker on GitHub or via e-mail.

If you encounter any bugs while using KNOSSOS, please tell us as well.

Integrating KNOSSOS

KNOSSOS can be integrated into your existing workflow, either by using the main program or its Python plugin interface.

You can contact us if you need help in writing Python plugins for KNOSSOS or if you have an idea how to improve KNOSSOS’ integration with your workflow.

General Assistance

We want to work closely together with other research teams in the connectomics field. Feel free to contact us if you need any technical assistance in this area or if you want to exchange ideas.

About us

KNOSSOS is developed by a group of students from Heidelberg University and Karlsruhe Institute of Technology, employed at Max Planck Institute for Medical Research.

Ph.D. Student

Jörgen Kornfeld

Ph.D. Student

Ph.D. Student

Fabian Svara

Ph.D. Student

Master Student of Computer Science

My-Tien Nguyen

Master Student of Computer Science

Bachelor Student of Computer Science

Norbert Pfeiler

Bachelor Student of Computer Science

Bachelor Student of Computer Science

Michael Pronkin

Bachelor Student of Computer Science

Bachelor Graduate of Computational Linguistics

Sebastian Spaar

Bachelor Graduate of Computational Linguistics

Bachelor Student of Computer Science

Alex Stepanov

Bachelor Student of Computer Science


We would like to thank the following persons for their contributions towards KNOSSOS:

Andreas Knecht, Claus Ripp, Konrad Kühne, Matthias Wegner, Oren Shatz, Patrick Müller

Contact us

If you have any questions or suggestions regarding KNOSSOS, feel free to write us:

[email protected]