SmartEM: machine learning-guided electron microscopy

Abstract

Connectomics provides nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole-brain or even whole-circuit reconstruction. To date, machine learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the continual computational improvements in processing EM images, acquiring EM images will become the rate-limiting step in automated connectomics. Here, in order to speed up EM imaging, we integrate machine learning into real-time image acquisition in a single-beam scanning electron microscope. This SmartEM approach allows an electron microscope to perform data-aware imaging of specimens. SmartEM saves time by allocating the proper imaging time for each region of interest-first scanning all pixels rapidly and then rescanning more slowly only the small subareas where a higher quality signal is required. We demonstrate that SmartEM achieves up to an ~7-fold acceleration of image acquisition time for connectomic samples using a commercial single-beam SEM in samples from nematodes, mice and human brain. We apply this fast imaging method to reconstruct a portion of mouse cerebral cortex with an accuracy comparable to traditional electron microscopy.