A Deep Metric for Multimodal Registration

Martin Simonovsky (1, 2, 3) , Benjamín Gutiérrez-Becker (4) , Diana Mateus (4) , Nassir Navab (4) , Nikos Komodakis (2, 3, 1)

1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 ENPC - École des Ponts ParisTech
3 imagine [Marne-la-Vallée]
4 CAMPAR - Computer Aided Medical Procedures & Augmented Reality

Multimodal registration is a challenging problem in medical imaging 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.

Domaines: Imagerie médicale

Martin Simonovsky, Benjamín Gutiérrez-Becker, Diana Mateus, Nassir Navab, Nikos Komodakis. A Deep Metric for Multimodal Registration. 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), Oct 2016, Athènes, Greece. pp.10-18, ⟨10.1007/978-3-319-46726-9_2⟩. ⟨hal-01576914⟩