Linear and Deformable Image Registration with 3D Convolutional Neural Networks

Stergios Christodoulidis, Mihir Sahasrabudhe, Maria Vakalopoulou, Guillaume Chassagnon, Marie-Pierre Revel, Stavroula Mougiakakou, Nikos Paragios

Abstract:

Image registration and in particular deformable registration
methods are pillars of medical imaging. Inspired by the recent advances
in deep learning, we propose in this paper, a novel convolutional neural
network architecture that couples linear and deformable registration
within a unified architecture endowed with near real-time performance.
Our framework is modular with respect to the global transformation
component, as well as with respect to the similarity function while it
guarantees smooth displacement fields. We evaluate the performance of
our network on the challenging problem of MRI lung registration, and
demonstrate superior performance with respect to state of the art elastic
registration methods. The proposed deformation (between inspiration &
expiration) was considered within a clinically relevant task of interstitial
lung disease (ILD) classification and showed promising results.