Segmentation-inspired Image Registration

Artificial intelligence has been used with great success for the segmentation of anatomical structures in medical imaging. We use these achievements to improve classical registration schemes. Particularly, we derive geometrical features such as centroids and principal axes of segments and use those in a combined approach. A smart filtering of the features results in a two phase preregistration, followed in a third phase by an intensity guided registration. We also propose to use a regularization, which enables a coupling of all components of the 3D transformation in a unified framework. Finally, we show how easily our approach can be applied even to challenging 3D medical data.