Share and organize large image datasets in one place

Import your data and send secured links to your collaborators.Fine-grain access controls included.


WEBKNOSSOS for Imaging Facilities


Visualize massive datasets–from anywhere

Upload your 2D or 3D datasets to WEBKNOSSOS and access them from wherever you have an internet connection. Enjoy the fast browsing speeds of WEBKNOSSOS.

WEBKNOSSOS works with all sorts of electron microscopy images, X-ray tomographies (CT), fluorescence microscopy images, and MRI.

Example: EM data from Motta et al. 2019, segmentation by scalable minds

Securely share data with collaborators

Invite collaborators or annotators into your organization. Manage users in teams and set role-based dataset permissions.

Send token-protected links to outside collaborators or reviewers.

The data stays safe in WEBKNOSSOS: By default users are not allowed to download the data unless you enable it.


Analyze your data directly online

Take measurements within your data. Mark interesting locations or bounding boxes. Create segmentations with manual brush or trace tools. Visualize segmented objects as mesh through the integrated mesh generation.

Create skeleton annotations of neurons, measure their path lengths and organize these skeletons in hierarchical groups. Try out the unique flight mode for high-speed tracing of axons or dendrites.

Use the task/project system to manage annotation projects. If you don't have annotators, you can hire our annotation services directly through WEBKNOSSOS.

Example: Annotations and EM data from Schmidt et al. 2017

Publish datasets to the community–when ready

Tell your story with data. Link directly from a figure in your publication to that location in WEBKNOSSOS. Readers will be able to explore your annotations and understand the context of your findings. 

WEBKNOSSOS is an excellent platform for publishing large datasets including segmentations and training data. Viewers can freely browse through your data and build upon it. 

Example: Figures with short-links from Motta et al. 2019 (Science)


Automate your work with our Python library

Use our free WEBKNOSSOS Python library to up/download datasets and annotations, work with WEBKNOSSOS datasets locally, and convert image stacks. The Python library seamlessly integrates WEBKNOSSOS in your existing data science workflows for training data generation, machine learning model training and volumetric data visualization.
Read the documentation.

Example: Skeleton approximation of an automatically segmented neuronEM data from Motta et al. 2019, segmentation by scalable minds

Interoperate with your favorite tools

WEBKNOSSOS supports a growing list of data formats for import. Download your data and annotations from WEBKNOSSOS to work with them in other tools. WEBKNOSSOS supports standard formats (e.g. TIFF, STL, N5/ZARR, CSV) for exports.

Work with the WEBKNOSSOS file formats in Python or MATLAB, with our open-source libraries. Learn more in the user documentation.


Support for many modalities

WEBKNOSSOS works with all sorts of 2D and 3D image modalities including multi-channel data.

    Scanning electron microscopy (SEM)
    Transmission electron microscopy (TEM)
    Serial block-face scanning electron microscopy (SBEM)
    Focused ion beam scanning electron microscopy (FIB-SEM)
    Serial section electron microscopy (ssSEM, S3EM, ssTEM)
    X-ray tomography (CT) and Micro-CT (µCT)
    Magnetic resonance imaging (MRI)
    Fluorescence microscopy

Examples (from left): Fluorescence microscopy from Drawitsch et al. 2018, Synchrotron X-Ray Tomography from Kuan et al. 2020 and MRI from Lüsebrink et al. 2017

Get started and upload your first dataset for free

A story about collaboration, continents, and the struggles of big data

Embark on a journey with two scientists in London as they navigate the challenges of managing vast datasets from a serial electron microscope. Witness their discovery of WEBKNOSSOS, the tool that will streamline their data analysis and collaboration, even as their research takes them across continents. Watch on YouTube

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