Progressively growing convolutional networks for end-to-end deformable image registration

Deformable image registration is often a slow process when using conventional methods. To speed up deformable registration, there is growing interest in using convolutional neural networks. They are comparatively fast and can be trained to estimate full-resolution deformation fields directly from pairs of images. Because deep learning-based registration methods often require rigid or affine pre-registration of the images, they do not perform true end-to-end image registration. To address this, we propose a progressive training method for end-to-end image registration with convolutional networks. The network is first trained to find large deformations at a low resolution using a smaller part of the full architecture. The network is then gradually expanded during training by adding higher resolution layers that allow the network to learn more fine-grained deformations from higher resolution data. By starting at a lower resolution, the network is able to learn larger deformations more quickly at the start of training, making pre-registration redundant. We apply this method to pulmonary CT data, and use it to register inhalation to exhalation images. We train the network using the CREATIS pulmonary CT data set, and apply the trained network to register the DIRLAB pulmonary CT data set. By computing the target registration error at corresponding landmarks we show that the error for end-to-end registration is significantly reduced by using progressive training, while retaining sub-second registration times.