M. Simonovsky, B. Gutierrez-Becker, D. Mateus, N. Navab, N. Komodakis A Deep Metric for Multimodal Registration Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016 (bib)
	Multimodal registration is a challenging problem due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution than can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin. (Extended version available in arxiv) http://arxiv.org/abs/1609.05396
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