Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy

Kisuk Lee, Nicholas Turner, Thomas Macrina, Jingpeng Wu, Ran Lu, Hyunjune Sebastian Seung

Research output: Contribution to journalReview article

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)188-198
Number of pages11
JournalCurrent Opinion in Neurobiology
Volume55
DOIs
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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