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.org.

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
Cell type specific labeling and partial connectomes of dopaminergic circuits reveal non-synaptic communication and large-scale axonal remodeling after exposure to cocaine https://doi.org/10.1101/2020.09.29.318881
Permeabilization-free en bloc immunohistochemistry for correlative microscopy https://doi.org/10.7554/eLife.63392
Multi-modal imaging of a single postmortem mouse brain over five orders of magnitude of resolution https://doi.org/10.1016/j.neuroimage.2021.118250
Primate neuronal connections are sparse as compared to mouse https://doi.org/10.1101/2020.09.24.311852
Three-dimensional Fib-Sem reconstruction of microtubule-organelle interaction in whole primary mouse beta cells https://doi.org/10.1083/jcb.202010039
Synapse-specific direction selectivity in retinal bipolar cell axon terminals https://doi.org/10.1101/2020.10.12.335810
Imaging Plant Cells by High-Pressure Freezing and Serial block-face scanning electron microscopy https://doi.org/10.1007/978-1-0716-0767-1_7
Image Processing for Volume Electron Microscopy https://doi.org/10.1007/978-1-0716-0691-9_13
Learning cellular morphology with neural networks https://doi.org/10.1038/s41467-019-10836-3
EM connectomics reveals axonal target variation in a sequence-generating network https://doi.org/10.7554/eLife.24364
Automated synaptic connectivity inference for volume electron microscopy https://doi.org/10.1038/nmeth.4206
Volume Electron Microscopic Analyses in the Larval Zebrafish https://doi.org/10.11588/heidok.00022556
When complex neuronal structures may not matter https://doi.org/10.7554/eLife.23508
Connectivity map of bipolar cells and photoreceptors in the mouse retina https://doi.org/10.7554/eLife.20041
3-dimensional electron microscopic imaging of the zebrafish olfactory bulb and dense reconstruction of neurons https://doi.org/10.1038/sdata.2016.100
Species-specific wiring for direction selectivity in the mammalian retina https://doi.org/10.1038/nature18609
Dense EM-based reconstruction of the interglomerular projectome in the zebrafish olfactory bulb https://doi.org/10.1038/nn.4290
Connectomic reconstruction of the inner plexiform layer in the mouse retina https://doi.org/10.1038/nature12346
High-accuracy neurite reconstruction for high-throughput neuroanatomy https://doi.org/10.1038/nn.2868
Wiring specificity in the direction-selectivity circuit of the retina https://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 e2006, ek0563, and j0256.

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 global team of open source contributors — get in touch with us if you want to help!

Research Group Leader

Jörgen Kornfeld

Research Group Leader

CEO, ariadne.ai (Germany) GmbH

Fabian Svara

CEO, ariadne.ai (Germany) GmbH

M.Sc., Computer Science

My-Tien Nguyen

M.Sc., Computer Science

Software Engineer, ariadne.ai (Germany) GmbH

Norbert Pfeiler

Software Engineer, ariadne.ai (Germany) GmbH

B.A., Computational Linguistics

Sebastian Spaar

B.A., Computational Linguistics


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

Konrad Kühne, Andreas Knecht, Patrick Müller, Michael Pronkin, Claus Ripp, Oren Shatz, Alexander Stepanov, Matthias Wegner

Contact us

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

[email protected]