@inproceedings{27a9d7315ced4ab6b0f61fb705c5db8a,
title = "A deep metric for multimodal registration",
abstract = "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 that 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.",
author = "Martin Simonovsky and Benjam{\'i}n Guti{\'e}rrez-Becker and Diana Mateus and Nassir Navab and Nikos Komodakis",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46726-9\_2",
language = "English",
isbn = "9783319467252",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "10--18",
editor = "Leo Joskowicz and Sabuncu, \{Mert R.\} and William Wells and Gozde Unal and Sebastian Ourselin",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
}