Unsupervised Learning for Large Motion Thoracic CT Follow-Up Registration

A. Hering and S. Heldmann

in: Medical Imaging, volume 10949 of Proceedings of the SPIE, 2019, page 109491B

DOI

Abstract

Image registration is the process of aligning two or more images to achieve point-wise spatial correspondence. Typically, image registration is phrased as an optimization problem w.r.t. a spatial mapping that minimizes a suitable cost function and common approaches estimate solutions by applying iterative optimization schemes such as gradient descent or Newton-type methods. This optimization is performed independently for each pair of images, which can be time consuming. In this paper we present an unsupervised learning-based approach for deformable image registration of thoracic CT scans. Our experiments show that our method performs comparable to conventional image registration methods and in particular is able to deal with large motions. Registration of a new unseen pair of images only requires a single forward pass through the network yielding the desired deformation field in less than 0.2 seconds. Furthermore, as a novelty in the context of deep-learning-based registration, we use the edge-based normalized gradient fields distance measure together with the curvature regularization as a loss function of the registration network.

A pdf file of this publication is available for personal use. Enter your e-mail address in the box below and press the button. You will receive an e-mail message with a link to the pdf file.