End-to-end solution for connectomic reconstruction
Reconstructing a connectome from volume electron microscopy data is a complex task. With over 10 years of experience in connectomics, our team leverages advanced machine learning techniques and cutting-edge technology to deliver precise and reliable results. Learn more in this page.
Our process
1. Alignment
A good alignment and registration are crucial to allow accurate data interpretation. Read more about our alignment process.
2. Neuron segmentation
We run our ML models and evaluate the results. We iterate on the parameters until achieving satisfactory results.
3. Synapse detection
Similar to neuron segmentation, we use ML to detect synapses. Model performance is evaluated through recall metrics.
4. Connectome & Results
Finally, we generate the connectome. We'll provide various deliverables, such as interactive connectivity map, statistics, 3D meshes, etc.
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.
We rolled out our ML pipeline for automated image alignment, neuron segmentation, and connectome reconstruction. The workflows instantly worked out well for the mouse dataset. 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.
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. Optional 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 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!
Pricing and duration
We offer fair prices and fast delivery times. Our goal is to make science accessible to labs of all sizes, and we understand the importance of meeting publication deadlines.
Every project includes:● Alignment● Neuron reconstruction● Synapse detection● Connectome generation
Custom deliverables can also be added (additional costs will apply), such as:● PSD area● spinehead volume● neuron type detection● and more.
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.
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.