We evaluated the performance of uniGradICON on the ACDC dataset [1] by registering the MRIs from the first time point to all subsequent time points. As an example, we've included the target images and the corresponding warped images below.
We evaluated the performance of uniGradICON on the 4DCT dataset [2] by registering the CTs from the first time point to all subsequent time points. As an example, we've included the target images and the corresponding warped images below.
You can acieve
Here is the qualitative results we presented in the paper.
[1] Bernard, Olivier, et al. "Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?." IEEE transactions on medical imaging 37.11 (2018): 2514-2525. doi: 10.1109/TMI.2018.2837502
[2] Castillo, Richard, et al. "A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets." Physics in Medicine & Biology 54.7 (2009): 1849.