The CellOrganizer project provides tools for
- learning generative models of cell organization directly from images
- storing and retrieving those models
- synthesizing cell images (or other representations) from one or more models
Model learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies.
- Tao Peng, Wei Wang, G. K. Rohde, R. F. Murphy (2009) Instance-Based Generative Biological Shape Modeling. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging (ISBI 2009), pp. 690-693.
- Gustavo Rohde, W. Wang, T. Peng, and R.F. Murphy (2008). Deformation-Based Nonlinear Dimension Reduction: Applications To Nuclear Morphometry. Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging (ISBI 2008), pp. 500-503.
- Gustavo Rohde, A. Ribeiro, K. N. Dahl, and R. F. Murphy (2008). Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa Cells. Cytometry, 73A:341-350.
- Ting Zhao and R.F. Murphy (2007). Automated Learning of Generative Models for Subcellular Location: Building Blocks for Systems Biology. Cytometry 71A:978-990.