We developed SmartEM, a method that integrates machine learning directly into the image acquisition process of an electron microscope. By allocating imaging time in a specific manner — scanning quickly at first, then rescanning only critical areas more slowly — we are able to accelerate the mapping of neural circuits up to sevenfold without sacrificing accuracy.
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References
Lichtman, J. W., Pfister, H. & Shavit, N. The big data challenges of connectomics. Nat. Neurosci. 17, 1448–1454 (2014). A review article outlining how data acquisition was becoming a major bottleneck in the field, motivating the need for faster imaging technologies such as SmartEM.
Januszewski, M. et al. High-precision automated reconstruction of neurons with flood filling networks. Nat. Methods 15, 605–610 (2018). This paper is a key example of the machine learning advancements in data analysis that shifted the bottleneck in connectomics from analysis back to acquisition.
Shapson-Coe, A. et al. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 384, eadk4858 (2024). This paper represents the current state of the art in large-scale connectomics, highlighting the immense data volumes that necessitate the acceleration SmartEM provides.
Mi, L. et al. Learning guided electron microscopy with active acquisition. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Lecture Notes in Computer Science Vol. 12265 (Springer, 2020). This paper describes the foundational work on active acquisition that the SmartEM project directly evolved from, introducing the core concept of learning-guided microscopy.
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This is a summary of: Meirovitch, Y. et al. SmartEM: machine learning-guided electron microscopy. Nat. Methods https://doi.org/10.1038/s41592-025-02929-3 (2025).
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AI-guided electron microscopy accelerates brain mapping. Nat Methods 23, 28–29 (2026). https://doi.org/10.1038/s41592-025-02930-w
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DOI: https://doi.org/10.1038/s41592-025-02930-w
