A deep metric for multimodal registration
Simonovsky M, Gutiérrez-Becker B, Mateus D, Navab N, Komodakis N (2016)
Publication Type: Conference contribution
Publication year: 2016
Journal
Publisher: Springer Verlag
Book Volume: 9902 LNCS
Pages Range: 10-18
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN: 9783319467252
DOI: 10.1007/978-3-319-46726-9_2
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.
Involved external institutions
École des Ponts ParisTech (ENPC) / École Nationale des Ponts et Chaussées
France (FR)
Technische Universität München (TUM)
Germany (DE)
France (FR)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., & Komodakis, N. (2016). A deep metric for multimodal registration. In Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 10-18). Springer Verlag.
MLA:
Simonovsky, Martin, et al. "A deep metric for multimodal registration." Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Ed. Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin, Springer Verlag, 2016. 10-18.
BibTeX: Download