Manual annotations for Connectomics

Get quality-controlled annotations from our expert annotators without having to manage the project yourself.


Annotation services


Generate training data for segmentations

Create dense segmentations for bounding boxes within your dataset. This data will be used to train Machine Learning systems for automatic segmentation. Features of interest can be any biological object from dense neurites, nuclei, blood vessels, organelles to larger features such as brain regions, lesions or entire organs.

Example: Dense segmentation of neurites in EM images of a mouse cortex(EM data by Max Planck Institute for Brain Research, Motta et al. 2019)

Skeletonize neurons

Generate skeleton traces from neurons in your data. Either this can be used directly for scientific discovery or in conjunction with automated analysis, e.g. for evaluation purposes or as training data.

Example: Skeletonization of apical dendrites in the olfactory bulb of a mouse(EM data by Sensory Circuits and Neurotechnology Lab, The Francis Crick Institute)


Proof-read automatic segmentations

State-of-the-art automatic segmentation methods generate good segmentations, but still produce split and merge errors. These can be efficiently corrected through WEBKNOSSOS' proof-reading tools. 

Example: Fixing over-segmentations by joining falsely split segments(EM data by Max Planck Institute for Brain Research, Motta et al. 2019)

Need a neuron reconstruction ?

Check out automated segmentation services!

Data annotation, beyond Connectomics

With our experience and knowledge in generating annotations for nano-scale neuroscience, we are qualified for further scientific data annotations. 


Life sciences


Material Science




And more...

We are happy to set up custom annotation projects based on your specific needs. Our annotators are flexible and are eager to learn about new kinds of data and biological features. Please get in touch to discuss the specifics of your annotation needs.

Quality is our top priority

As the basis of automated analysis or scientific discovery, we understand that the quality of the annotations is most important.With the experience of many annotation projects, we have implemented several processes to ensure high quality of data.

Annotator selection and training

We select our annotators based on previous experience with biomedical images. We constantly improve our training material and provide detailed feedback on sample tasks. Additionally, we continuously monitor the work of each annotator.


Depending on the performed task and quality requirements, we perform the task redundantly by separate annotators. This can range between 2- and 5-fold redundancy. With our consensus technology, we will provide unified results to you.

Annotator and reviewer roles

In this mode, one person will perform the annotation job and another person will review and correct the work. Reviewers will be specifically qualified annotators, who have proven their reliability in previous projects.

How does it work?

You can watch everything happen on WEBKNOSSOS.


Upload your data and define tasks

With your data on WEBKNOSSOS, we will schedule an intro call to discuss your project's needs. Based on that, we will propose a project structure and estimate.


Monitor theproject's progress

All work is happening on WEBKNOSSOS. You will be able to review intermediate results and track the overall progress of the project from start to finish.


Receive results

The results will be available in WEBKNOSSOS from where you can continue working with them or download in your desired format.

Example cases


EM data by Sensory Circuits and Neurotechnology Lab, The Francis Crick Institute, London, UK

Skeletonizing apical dendrites in EM data

In this project, our annotators generated skeletonizations of apical dendrites in the olfactory bulb of a mouse. The raw data was serial-blockface electron microscopy (SBEM) images. The annotators were presented with pre-seeded somata and their task was to find the apical dendrite and accurately generate skeletons from the soma in the mitral cell layer (MCL) to their ending in the glomular layer (GL).

    301 apical dendrites
    1 month delivery time
    5-fold redundancy for quality assurance
    3,000 EUR budget

Segmentation by scalable minds

Dense neuron segmentation

For this project, a dense reconstruction of neural tissue was needed. Our annotators generated training data thanks to WEBKNOSSOS’ volume annotation tools within several bounding boxes of the raw dataset. Special attention was given to annotate all branches in the neurons and carefully segment even the smallest and thinnest processes.

    2 month delivery time
    Proof-reading and iterations for quality assurance
    6,000 EUR budget

Looking for Automated Image Analysis?

Check out our image analysis services. Reconstruct rich information from microscopy images with machine learning-based tools. Automate tasks such as image alignment, registration, dense segmentation, and object detection.