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) |
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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 |
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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