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)
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
Meterology
And more...
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.
Redundancy
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).
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.