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.
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