A new collection of synthetic data for the development and evaluation of registration methods has been released.
This collection of synthetic data is intended for the development and evaluation of methods for intra-subject registration in the presence of changing pathologies.
Specifically, we developed this data to evaluate our Geometric Metamorphosis registration method  for the quantification of tumor progression (e.g., estimating its infiltrating and displacing components). This method is also being applied to the quantification and prediction of chronic blood perfusion changes after Traumatic Brain Injuries.
In this data collection three variables are explored:
- Tumor location: near edge of brain versus in the center of brain.
- Tumor size: small versus large.
- Tumor type: highly infiltrative, mix of infiltrative and displacive, and highly displacive.
Figure 1. An example of a large, ring-enhancing, highly infiltrative tumor near the edge of the brain.
This data generation effort was supported in-part by:
- NIH/NIBIB grant “National Alliance of Medical Image Computing” (NA-MIC, PI: Kikinis, 1U54EB005149)
- NIH/NCI grant “Image Registration for Ultrasound-Based Neurosurgical Navigation” (TubeTK, PI: Wells, Aylward, 1R01CA138419)
- NIH/NCI grant “A Needle Guidance System for Hepatic Tumor Ablation that Fuses Real-Time Ultrasound” (InnerOptic, PI: Razzaque, R1R44CA143234-02A1)
 – Prastawa M, Bullitt E, Gerig G, “Simulation of Brain Tumors in MR Images for Evaluation of Segmentation Efficacy.” Medical Image Analysis (MedIA), Vol 13, No 2, April 2009, Pages 297-311. http://www.sci.utah.edu/~prastawa/papers/MedIA2009_Prastawa_TumorSim.pdf
 – Niethammer M, Hart G, Pace D, Vespa P, Irimia A, Van Horn J, Aylward S, “Geometric Metamorphosis.” Lecture Notes in Computer Science, Vol. 6893, Medical Image Computing and Computer Assisted Intervention (MICCAI) 2011. Pages 639-646 http://wwwx.cs.unc.edu/~mn/sites/default/files/niethammer2011_geometric_metamorphosis_miccai.pdf
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