TY - JOUR
T1 - Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
AU - Lee, Kisuk
AU - Turner, Nicholas
AU - Macrina, Thomas
AU - Wu, Jingpeng
AU - Lu, Ran
AU - Seung, Hyunjune Sebastian
N1 - Funding Information:
This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/ Interior Business Center (DoI/IBC) contract number D16PC0005, NIH/NIMH (U01MH114824, U01MH117072, RF1MH117815), NIH/NINDS (U19NS104648, R01NS104926), NIH/NEI (R01EY027036), ARO (W911NF-12-1-0594), and the Mathers Foundation. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government. We are grateful for assistance from Google, Amazon, and Intel.
Funding Information:
This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/ Interior Business Center (DoI/IBC) contract number D16PC0005 , NIH/NIMH ( U01MH114824 , U01MH117072 , RF1MH117815 ), NIH/NINDS ( U19NS104648 , R01NS104926 ), NIH/NEI ( R01EY027036 ), ARO ( W911NF-12-1-0594 ), and the Mathers Foundation . The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government. We are grateful for assistance from Google, Amazon, and Intel.
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/4
Y1 - 2019/4
N2 - 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.
AB - 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.
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U2 - 10.1016/j.conb.2019.04.001
DO - 10.1016/j.conb.2019.04.001
M3 - Review article
C2 - 31071619
AN - SCOPUS:85065093780
SN - 0959-4388
VL - 55
SP - 188
EP - 198
JO - Current Opinion in Neurobiology
JF - Current Opinion in Neurobiology
ER -