Name
Abstract | ||
|---|---|---|
Biological shape modeling is an essential task that is required for systems biology efforts to simulate complex cell behaviors. Statistical learning methods have been used to build generative shape models based on reconstructive shape parameters extracted from microscope image collections. However, such parametric modeling approaches are usually limited to simple shapes and easily-modeled parameter distributions. Moreover, to maximize the reconstruction accuracy, significant effort is required to design models for specific datasets or patterns. We have therefore developed an instance-based approach to model biological shapes within a shape space built upon diffeomorphic measurement. We also designed a recursive interpolation algorithm to probabilistically synthesize new shape instances using the shape space model and the original instances. The method is quite generalizable and therefore can be applied to most nuclear, cell and protein object shapes, in both 2D and 3D. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISBI.2009.5193141 | ISBI |
Keywords | Field | DocType |
nuclear shape,instance-based generative biological shape,parametric modeling,shape interpolation,complex cell behaviors,cellular biophysics,reconstruction accuracy,biological object shapes,location proteomics,statistical analysis,cell shape,learning (artificial intelligence),microscopy,microscope image collections,protein shape,proteins,recursive interpolation algorithm,biology computing,molecular biophysics,biological shape modeling,complex cell behavior,new shape instance,shape space,reconstructive shape parameters,biological shape,diffeomorphic measurement,simple shape,recursive estimation,reconstructive shape parameter,machine learning,statistical learning,generative models,generative shape model,instance-based generative modeling,protein object shape,shape space model,probability,parametric model,biomedical research,interpolation,bioinformatics,bandwidth,shape,system biology,shape parameter,kernel,indexing terms,image reconstruction,learning artificial intelligence | Iterative reconstruction,Kernel (linear algebra),Active shape model,Computer vision,Parametric model,Pattern recognition,Computer science,Interpolation,Systems biology,Artificial intelligence,Recursion,Shape analysis (digital geometry) | Conference |
Volume | ISSN | ISBN |
5193141 | 1945-7928 | 978-1-4244-3932-4 |
Citations | PageRank | References |
4 | 0.45 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Peng | 1 | 61 | 7.57 |
| Wei Wang | 2 | 104 | 7.88 |
| Gustavo K. Rohde | 3 | 395 | 41.81 |
| Robert F Murphy | 4 | 851 | 78.19 |