Visualize your data–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

Create and visualize segmentations

Create volume annotations (i.e. segmentations) with manual brush or trace tools. Download the annotations to train a machine learning model or for visualization purposes.  

Visualize segmented objects as mesh through the integrated mesh generation. Explore dense segmentations with colored and patterned maps. 

Get annotation help from your collaborators. Use the task/project system to distribute tasks to multiple annotators. If you don't have annotators, you can hire our annotation services directly through WEBKNOSSOS.

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

Skeletonize, measure, and quantify neurons 

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. Trained annotator crowds achieve tracing speeds of 1.5 ± 0.6 mm/h for axons and 2.1 ± 0.9 mm/h for dendrites in 3D electron microscopy data. Read more in the WEBKNOSSOS paper.

Collaborate on annotations with your colleagues. Use the task/project system to distribute tasks to multiple annotators. 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

Generate training and evaluation data

Use the volume annotation features to generate training data for a segmentation model or pixel classifier.

Use the skeleton tools to generate neuron evaluation data. The comment function is a flexible way of marking locations in the data, e.g., for seeding, or classification of segments.

Get annotation help from your collaborators. Use the task/project system to distribute tasks to multiple annotators. If you don't have annotators, you can hire our annotation services directly through WEBKNOSSOS.

Example: Ground truth volume annotations from Berning et al. 2015

Visualize predictions and segmentations

Add prediction maps and color-coded segmentation layers to your dataset for debugging your work. Create and apply mappings (combining multiple segments into one) to evaluate agglomeration strategies.

Visually inspect prediction errors with the context of the raw data and other channels. Use the histogram to manually select classification thresholds.

Generate mesh visualizations of your segmentations (see screenshot above) or skeleton approximations (see screenshot below).

Example: EM data from Motta et al. 2019, segmentation (left) and affinity predictions (right) by scalable minds

Proof-read automatic reconstructions

Correct split and merge errors with the proofreading tools. Visualize your segmented objects as 3D meshes and proofread directly in the 3D viewport. Highlight the segments forming an agglomerate by hovering to identify potential edits. Use Merger Mode to fix over-segmentations. Use the skeleton tools to quickly connect segments. Use the volume annotation features to correct any errors in your automated segmentation.

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

Connectome viewer

Explore your reconstruction with the connectome viewer.
Instantly access synaptic connections and partners by selecting a segment, and seamlessly navigate to the precise location of synapses within the EM data through a single click.

Example: Loomba et al., Science 2022

Interoperate with your favorite tools

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.

Automate workflows with Python

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 into your existing data science workflows for training data generation, machine learning model training, and volumetric data visualization.
Read the documentation.

Publish your data alongside your publication

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 because readers can freely browse through your data and build upon it. 

Example: Figures with wklink.org short-links from Karimi et al. 2020

Never lose your work

WEBKNOSSOS auto-saves annotations every 30s and keeps a versioned history of all annotations. Correcting a mistake is just one click away.

Annotation data on WEBKNOSSOS is externally backed up daily. For paid plans, there is an option to back up datasets as well.

You can always download your annotations or datasets in accessible formats.

Example: EM data and annotations from Helmstaedter et al. 2013