RoboEM, an artificial intelligence (AI)-based flight agent, automatically steers through three-dimensional electron microscopy (3D-EM) images of brain tissue to follow neurites. RoboEM substantially improves state-of-the-art automated reconstructions, eliminating manual proofreading needs in complex connectomic analysis problems and paving the way for high-throughput, cost-effective, large-scale mapping of neuronal networks — connectomes.
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References
Briggman, K. L. & Bock, D. D. Volume electron microscopy for neuronal circuit reconstruction. Curr. Opin. Neurobiol. 22, 154–161 (2012). A review article comparing the key 3D-EM imaging methods for connectomics.
Dorkenwald, S. et al. Neuronal wiring diagram of an adult brain. Preprint at bioRxiv https://doi.org/10.1101/2023.06.27.546656 (2023). This paper reports a major whole-brain connectomic reconstruction project using the latest AI plus massive human annotation.
Boergens, K. M. et al. webKnossos: efficient online 3D data annotation for connectomics. Nat. Methods 14, 691–694 (2017). This paper reports a browser-based tool maximizing human annotation speed by self- centered flight viewing.
Januszewski, M. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605–610 (2018). This paper reports a state-of-the-art AI-based 3D-EM data analysis method for connectomics.
Sheridan, A. et al. Local shape descriptors for neuron segmentation. Nat. Methods 20, 295–303 (2023). This paper reports a state-of-the-art AI-based 3D-EM data analysis method for connectomics with increased efficiency.
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This is a summary of: Schmidt, M., Motta, A., Sievers, M. & Helmstaedter, M. RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat. Methods https://doi.org/10.1038/s41592-024-02226-5 (2024).
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Building an automated three-dimensional flight agent for neural network reconstruction. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02227-4
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DOI: https://doi.org/10.1038/s41592-024-02227-4
