TY - GEN
T1 - Part-wise neuron segmentation using artificial templates
AU - Gulyanon, S.
AU - Sharifai, N.
AU - Kim, M. D.
AU - Chiba, A.
AU - Tsechpenakis, G.
N1 - Funding Information:
This work is supported by NSF/DBI [#1252597]: ‘CAREER: Modeling the structure and dynamics of neuronal circuits in the Drosophila larvae using image analytics’ awarded to G. Tsechpenakis.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - Our goal is to analyze neuronal morphology during development using artificially created templates. Such templates serve as input from the domain expertise: a standardized representation of the neuron, independent from imaging modalities and resolution, is what a neurobiologist can provide from knowledge and qualitative observations in the microscope. A template is divided into context-specific (topology, shape, and/or function) compartments, and our task is to segment input neuron volumes from their surroundings and partition them accordingly. We solve this problem using a global-to-local approach. We first employ a global transformation that serves as part-wise alignment of the template with the input volume. Then, we use local, deformable compartment shape registration using MRF-based free-form deformations. We validated our results using aCC motorneuron image stacks from larva Drosophila, at multiple developmental instances and different spatial resolutions.
AB - Our goal is to analyze neuronal morphology during development using artificially created templates. Such templates serve as input from the domain expertise: a standardized representation of the neuron, independent from imaging modalities and resolution, is what a neurobiologist can provide from knowledge and qualitative observations in the microscope. A template is divided into context-specific (topology, shape, and/or function) compartments, and our task is to segment input neuron volumes from their surroundings and partition them accordingly. We solve this problem using a global-to-local approach. We first employ a global transformation that serves as part-wise alignment of the template with the input volume. Then, we use local, deformable compartment shape registration using MRF-based free-form deformations. We validated our results using aCC motorneuron image stacks from larva Drosophila, at multiple developmental instances and different spatial resolutions.
KW - Free-form deformations
KW - Markov Random Fields
KW - Neuron morphology
KW - Registration
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85048078542&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048078542&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2018.8363656
DO - 10.1109/ISBI.2018.8363656
M3 - Conference contribution
AN - SCOPUS:85048078542
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 640
EP - 644
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -