NBCR is pleased to offer a Summer Training Program (STP) August 1st to 5th, 2016, on the theme of image-based meshing and structural modeling. This week-long intensive program will introduce 12-20 students to principles, methods, and NBCR tools for generating high-quality three-dimensional meshes for numerical analysis in multi-scale modeling of subcellular, cell, tissue, and organ biophysics.
Structural models will be derived from structural data obtained primarily from 3D imaging modalities, including electron tomography, multi-photon and confocal microscopy, and whole-body medical imaging modalities like CT and MRI.
The course will include:
1. Automated and manual image segmentation and annotation strategies;
2. improving the quality of surface meshes and generating volumetric meshes with GAMer;
3. using Hex-Blender to construct 3D models;
4. developing high-order finite-element meshes, including patient-specific organ models with Continuity;
5. using Kepler workflows to streamline analysis.
The resulting meshes will be suitable for a range of biophysical modeling applications from stochastic Monte Carlo and Brownian Dynamics models to subcellular and whole-cell transport models to whole-organ biomechanics and electrophysiology that will be the topics of future NBCR training programs. In consultation with the course organizers, those accepted into the program will be encouraged to bring and work with their own data sets.
Dates: August 1st – 5th, 2016
Venue: University of California San Diego, La Jolla, CA, USA
Organizers: Andrew McCulloch, Mark Ellisman, Thomas Bartol, and Mike Holst
| Instructors | Contact | |
| Thomas M Bartol, Eric Carruth, Chris Churas, Andrew McCulloch, Alex Perez, Chris Villongco, and Kevin Vincent |
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Registration Info
Registration for the workshop is now open – please register using https://www.regonline.com/NBCR2016
Registration fee: $200
Deadline: July 25, 2016.
No refunds or cancellations
Program
The materials for this workshop, including videos of the lectures and tutorials, are available through the Biomedical Big Data Training Collaborative.
Those with accounts on the site can directly access the course via this link.