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Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy.
- Source :
-
Current Opinion in Neurobiology . Apr2019, Vol. 55, p188-198. 11p. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09594388
- Volume :
- 55
- Database :
- Academic Search Index
- Journal :
- Current Opinion in Neurobiology
- Publication Type :
- Academic Journal
- Accession number :
- 136768807
- Full Text :
- https://doi.org/10.1016/j.conb.2019.04.001