Unsupervised learning for large motion thoracic CT follow-up registration

Alessa Hering; Stefan Heldmann

doi:10.1117/12.2506962

15 March 2019 Unsupervised learning for large motion thoracic CT follow-up registration

Alessa Hering, Stefan Heldmann

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109491B (2019) https://doi.org/10.1117/12.2506962
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Alessa Hering and Stefan Heldmann "Unsupervised learning for large motion thoracic CT follow-up registration", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491B (15 March 2019); https://doi.org/10.1117/12.2506962

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