Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far. Convolutional nets were first applied to neuronal boundary detection a dozen years ago, and have now achieved impressive accuracy on clean images. Robust handling of image defects is a major outstanding challenge. Convolutional nets are also being employed for other tasks in neural circuit reconstruction: finding synapses and identifying synaptic partners, extending or pruning neuronal reconstructions, and aligning serial section images to create a 3D image stack. Computational systems are being engineered to handle petavoxel images of cubic millimeter brain volumes.
@article{arxiv.1904.12966,
title = {Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy},
author = {Kisuk Lee and Nicholas Turner and Thomas Macrina and Jingpeng Wu and Ran Lu and H. Sebastian Seung},
journal= {arXiv preprint arXiv:1904.12966},
year = {2019}
}