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 | |
| 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 |
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Keywords
- Free-form deformations
- Markov Random Fields
- Neuron morphology
- Registration
- Segmentation
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Part-wise neuron segmentation using artificial templates. / Gulyanon, S.; Sharifai, N.; Kim, M. D.; Chiba, Akira; Tsechpenakis, G.
2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Vol. 2018-April IEEE Computer Society, 2018. p. 640-644.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Part-wise neuron segmentation using artificial templates
AU - Gulyanon, S.
AU - Sharifai, N.
AU - Kim, M. D.
AU - Chiba, Akira
AU - Tsechpenakis, G.
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
VL - 2018-April
SP - 640
EP - 644
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
ER -