TY - JOUR
T1 - Part-based motor neuron recognition in the Drosophila ventral nerve cord
AU - Chang, Xiao
AU - Kim, Michael D.
AU - Stephens, Rachel
AU - Qu, Tiange
AU - Chiba, Akira
AU - Tsechpenakis, Gavriil
N1 - Funding Information:
This work is supported in part by NSF/DBI [# 1252597 ]: ‘CAREER: Modeling the structure and dynamics of neuronal circuits in the Drosophila larvae using image analytics’, and NSF/DBI [# 1062405 ]: ‘ABI Innovation: Modeling the Drosophila Brain with Single-neuron Resolution using Computer Vision Methods’, awarded to G. Tsechpenakis.
PY - 2014/4/15
Y1 - 2014/4/15
N2 - 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, where compartment labels are given, and (ii) latent-state learning, where compartment labels are not given in the samples. We demonstrate the accuracy of our approach using wild-type motor neurons in the larval ventral nerve cord. However, our method can also be used for the identification of motor neuron mutations, as well as the automated annotation of the motor circuitry in wild type and mutant animals. Our method is directly applicable to the recognition of compartment-defined structures.
AB - 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, where compartment labels are given, and (ii) latent-state learning, where compartment labels are not given in the samples. We demonstrate the accuracy of our approach using wild-type motor neurons in the larval ventral nerve cord. However, our method can also be used for the identification of motor neuron mutations, as well as the automated annotation of the motor circuitry in wild type and mutant animals. Our method is directly applicable to the recognition of compartment-defined structures.
KW - Drosophila
KW - Latent-state Conditional Random Fields
KW - Motor neuron classification
KW - Part-based recognition
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U2 - 10.1016/j.neuroimage.2013.12.023
DO - 10.1016/j.neuroimage.2013.12.023
M3 - Article
C2 - 24373882
AN - SCOPUS:84893487877
VL - 90
SP - 33
EP - 42
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -