Qualitative Results of uniGradICON

ACDC Dataset

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.

DirLab 4DCT Dataset

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

Examples in the paper

Here is the qualitative results we presented in the paper.

References

[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

[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. DOI