Motor neuron recognition in the Drosophila ventral nerve cord

X. Chang, Michael D. Kim, A. Chiba, G. Tsechpenakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We exploit the morphological stereotypy and relative simplicity of the Drosophila nervous system to model the diverse neuronal morphologies of individual motor neurons and understand underlying principles of synaptic connectivity in a motor circuit. In our analysis, we use images depicting single neurons labeled with green fluorescent protein (GFP) and serially imaged with laser scanning confocal microscopy. We model morphology with a novel formulation of Conditional Random Fields, a hierarchical latent-state CRF, to capture the highly varying compartment-based structure of the neurons (soma-axon-dendrites). In the training phase, we follow two approaches: (i) hierarchical learning, were compartment labels are given, and (ii) latent-state learning, where compartment labels are not given in the training samples. We demonstrate the accuracy of our approach using wild-type MNs in the larval ventral nerve cord. However, our method can also be used for the identification of MN mutations, as well as the automated annotation of the motor circuitry in wild type and mutant animals.

Original languageEnglish (US)
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages1488-1491
Number of pages4
DOIs
StatePublished - Aug 22 2013
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: Apr 7 2013Apr 11 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
CountryUnited States
CitySan Francisco, CA
Period4/7/134/11/13

Keywords

  • Drosophila
  • latent state Conditional Random Fields
  • neuron morphology

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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