A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb.

Journal: Journal of neuroscience methods

Published Date: Jan 15, 2019

Abstract

BACKGROUND: The Allen Mouse Brain Atlas allows study of the brain's molecular anatomy at cellular scale, for thousands genes. To fully leverage this resource, one must register histological images of brain tissue - a task made challenging by the brain's structural complexity and heterogeneity, as well as inter-experiment variability.

Authors

Alexander Andonian

Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Daniel Paseltiner

Bates College, Program in Neuroscience, Lewiston, ME 04240, United States.
Travis J Gould

Bates College, Physics Department, Lewiston, ME 04240, United States.
Jason B Castro

Bates College, Program in Neuroscience, Lewiston, ME 04240, United States. Electronic address: jcastro@bates.edu.

Keywords

Animals Atlases as Topic Cluster Analysis Deep Learning Image Processing, Computer-Assisted In Situ Hybridization Mice Olfactory Bulb Transcriptome

External Resources

View on PubMed Access via DOI PubMed (30529409)

A deep learning based method for large-scale classification, registration, and clustering of in-situ hybridization experiments in the mouse olfactory bulb.

Abstract

Authors

Keywords

External Resources

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