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Unsupervised learning for large motion thoracic CT follow-up registration
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Paper 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.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491B (15 March 2019); doi: 10.1117/12.2506962
Show Author Affiliations
Alessa Hering, Fraunhofer MEVIS (Germany)
Stefan Heldmann, Fraunhofer MEVIS (Germany)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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