Part-wise neuron segmentation using artificial templates

S. Gulyanon; N. Sharifai; Michael D. Kim; A. Chiba; G. Tsechpenakis

doi:10.1109/ISBI.2018.8363656

Part-wise neuron segmentation using artificial templates

S. Gulyanon, N. Sharifai, Michael D. Kim, A. Chiba, G. Tsechpenakis

Biology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Publisher	IEEE Computer Society
Pages	640-644
Number of pages	5
Volume	2018-April
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363656
State	Published - May 23 2018
Event	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States Duration: Apr 4 2018 → Apr 7 2018

Other

Other	15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country	United States
City	Washington
Period	4/4/18 → 4/7/18

Keywords

Free-form deformations
Markov Random Fields
Neuron morphology
Registration
Segmentation

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2018.8363656

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Cite this

Gulyanon, S., Sharifai, N., Kim, M. D., Chiba, A., & Tsechpenakis, G. (2018). Part-wise neuron segmentation using artificial templates. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (Vol. 2018-April, pp. 640-644). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363656

Gulyanon, S, Sharifai, N, Kim, MD, Chiba, A & Tsechpenakis, G 2018, Part-wise neuron segmentation using artificial templates. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. vol. 2018-April, IEEE Computer Society, pp. 640-644, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/4/18. https://doi.org/10.1109/ISBI.2018.8363656

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abstract = "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.",

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AU - Tsechpenakis, G.

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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.

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