DeformIt allows the user to simulate novel data from a single reference dataset (an image, its segmentation, and its landmark points) capturing the exact anatomy on which the developed algorithms need to be trained or validated. We employ a physically- and statistically-based generative model to deform the single reference dataset. Additionally, the reference dataset can be degraded by noise or non-uniformity intensities.
See also related project VascuSynth on simulating tree-like structures.
20110816: DeformIT now available at GITHUB https://github.com/erudianart/DeformIt/
Example 2D Simulation: (a) Reference image with different grid displacements (small arrows), (b) deformed checkerboard, (c) deformed images, (d) non-uniformity field, (e) ‘c+d’, (f) ‘e+noise’, (g) simulated segmentation (reference segmentation not shown
For more resutls see our paper below...
Ghassan Hamarneh, Preet Jassi, and Lisa Tang. Simulation of Ground-Truth Validation Data via Physically- and Statistically-based Warps. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pages 459-467, 2008.