Learning Iterative Optimisation for Deformable Image Registration of Lung CT with Recurrent Convolutional Networks

Abstract

Deep learning-based methods for deformable image registration have continually been increasing in accuracy. However, conventional methods using optimisation remain ubiquitous, as they often outperform deep learning-based methods regarding accuracy on test data. Recent learning-based methods for lung registration tasks prevalently employ instance optimisation on test data to achieve state-of-the-art performance. We propose a fully deep learning-based approach, that aims to emulate the structure of gradient-based optimisation as used in conventional registration and thus learns how to optimise. Our architecture consists of recurrent updates on a convolutional network with deep supervision. It uses a dynamic sampling of the cost function, hidden states to imitate information flow during optimisation and incremental displacements for multiple iterations. Our code is publicly available at https://github.com/multimodallearning/Learn2Optimise/.