Automated segmentations

Full service for reconstructing biological features from large-scale 3D microscopy datasets using AI technology. Neurons, Cell Organelles, Synapses, Lesions and more. Custom trained for your use case and data. Scalable from gigabyte to petabyte-scale datasets.

Automated segmentation services

Neuron Reconstruction

Our tooling has been developed for building powerful and custom AI models to reconstruct neurons, organelles and other features from biological microscopy data.
We offer a full service including training data generation and proof-reading in order to achieve high-quality results for your use cases.
● Neuron Segmentation● Axons & Dendrites Reconstruction● Synapse and Dendritic Spine Detection● Heuristical split and merge error handling● Annotation and proof-reading services available

Connectome Reconstruction

Using our Voxelytics Connect tooling, we offer a repeatable, automated approach to circuit reconstruction and generating Connectome matrices from raw electron microscopy data.
We collaborate closely with your group in order to make the most out of your data. As part of a full service, we optionally generate the required training data and proof-read the results.
● Neuron reconstruction and synapse detection● Heuristics for split and merge error fixes● Annotation and proof-reading services available● Ready for 3D volume electron microscopy● (SBEM, Multi-SEM. FIB-SEM, TEM)● Fully integrated, collaborative workflow in WEBKNOSSOS


Blood Vessel and Nuclei Segmentation

Segment large biological features, such as blood vessels, nuclei or somata, for analysis or masking purposes.

● Blood Vessel, Somata, and Nuclei Segmentation● No training data required● Excellent for operations requiring masks

Raw image data by Max Planck Institute for Brain Research

Didn't find what you were looking for?

Get in touch with us for custom automated segmentations for volume EM and X-Ray data.


How it works


1. Book an intro call

Discuss your research goals and data characteristics with us. Define the analysis tasks and arrange data access on WEBKNOSSOS.


2. Receive a free segmentation sample for your data

Once we have access to your data, we will perform a segmentation on a subsample of your data (typically 1 GVx). Based on this, we can discuss the next steps and evaluate the need for re-training.


3. Retraining for your data

We have a large selection of pre-trained models for various types of EM images. However, sometimes it is required to retrain models for particular image characteristics. In that case, our annotators can generate the required ground truth and we will train custom models for optimal results.


4. Automated processing

We roll out our machine learning pipeline Voxelytics on your data. The processing pipeline includes stack alignment, neuron segmentation, neurite type detection, nuclei/somata/blood vessel classification, synapse detection, and connectome assembly.


5. Polish your results in WEBKNOSSOS

Visualize and evaluate the results in WEBKNOSSOS. Use the advanced proofreading tools in WEBKNOSSOS to correct any remaining errors on the objects you care about. Benefit from the collaboration features to speed up this process.


6. Work on your scientific analysis

Explore the results in WEBKNOSSOS and use the available Python libraries for scientific analysis. Of course, you can download the data at any time!

Support your publications with rich visuals

Neuron reconstruction made by scalable minds with Voxelytics and WEBKNOSSOS. Raw SBEM data by Motta et al., Science 2019.

Case study

Neuron reconstruction of Human cortex from volume EM. Raw data by Loomba et al. (Science 2022). Segmentation, connectome, and animation by scalable minds.

Loomba et al. submitted a comparative study of neural structures in 8 Mouse, Macaque, and Human datasets (SBEM, each 0.5-2TB).
For that, a reliable, repeatable, highly-scalable solution for Connectome reconstruction across three different species with significant differences in cellular morphology was required.
The automated image alignment, neuron segmentation, and connectome reconstruction were carried out using Voxelytics.We applied the default alignment workflows of Voxelytics on the respective dataset without manual tweaking. The out-of-the-box neuron segmentation and synapse detection workflows worked well for the mouse datasets. Any parameter tweaks could be made in a config file and consecutive Voxelyics runs would only execute the changed workflow tasks.

To improve the reconstruction quality for the macaque and human tissue, we interactively re-trained the segmentation models with data labeled in WEBKNOSSOS. We decided on a best-performing configuration after using the integrated evaluation methods and rolled that out to the remaining datasets. All Voxelytics workflows were executed highly parallelized on an HPC and results were instantly available in WEBKNOSSOS for inspection.
Our declarative analysis approach, the repeatable workflows, and extensible task architecture allowed us to quickly iterate on the 8 datasets and derive insights in a manner of days. For scientists, Voxelytics moves the analysis burden from big data processing challenges to neurobiological analysis.

Some examples


Dense neuron segmentation of mouse layer 4 somatosensory cortex 

Full dense neuron instance segmentation using modified U-Nets and hierarchical agglomeration. Read blog post.


Synapse, vesicle, and mitochondria detection in cortex

CNN-based segmentation of all synapses, vesicles, and mitochondria in preparation for synaptic connectivity mapping.


Axon and dendrite classification

Integrate semantic segmentation of neuron subtypes (axon, dendrite, glia, etc) into the agglomeration to prevent merger error based on prior biological knowledge.