Abstract
2022 44th Annual International Conference of the IEEE Engineering
in Medicine & Biology Society (EMBC), 2022, pp. 3510-3513 Many applications in image-guided surgery and therapy require fast and
reliable non-linear, multi-modal image registration. Recently proposed
unsupervised deep learning-based registration methods have demonstrated
superior performance compared to iterative methods in just a fraction of the
time. Most of the learning-based methods have focused on mono-modal image
registration. The extension to multi-modal registration depends on the use of
an appropriate similarity function, such as the mutual information (MI). We
propose guiding the training of a deep learning-based registration method with
MI estimation between an image-pair in an end-to-end trainable network. Our
results show that a small, 2-layer network produces competitive results in both
mono- and multi-modal registration, with sub-second run-times. Comparisons to
both iterative and deep learning-based methods show that our MI-based method
produces topologically and qualitatively superior results with an extremely low
rate of non-diffeomorphic transformations. Real-time clinical application will
benefit from a better visual matching of anatomical structures and less
registration failures/outliers.