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Proceedings Paper

Mutual information for unsupervised deep learning image registration
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Paper Abstract

Current unsupervised deep learning-based image registration methods are trained with mean squares or nor- malized cross correlation as a similarity metric. These metrics are suitable for registration of images where a linear relation between image intensities exists. When such a relation is absent knowledge from conventional image registration literature suggests the use of mutual information. In this work we investigate whether mutual information can be used as a loss for unsupervised deep learning image registration by evaluating it on two datasets: breast dynamic contrast-enhanced MR and cardiac MR images. The results show that training with mutual information as a loss gives on par performance compared with conventional image registration in contrast enhanced images, and the results show that it is generally applicable since it has on par performance compared with normalized cross correlation in single-modality registration.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130R (10 March 2020); doi: 10.1117/12.2549729
Show Author Affiliations
Bob D. de Vos, Amsterdam Univ. Medical Ctr. (Netherlands)
Univ. Medical Ctr. Utrecht (Netherlands)
Bas H. M. van der Velden, Univ. Medical Ctr. Utrecht (Netherlands)
Jörg Sander, Amsterdam Univ. Medical Ctr. (Netherlands)
Univ. Medical Ctr. Utrecht (Netherlands)
Kenneth G. A. Gilhuijs, Image Sciences Institute, Univ. Medical Ctr. Utrecht (Netherlands)
Marius Staring, Leiden Univ. Medical Ctr. (Netherlands)
Ivana Išgum, Amsterdam Univ. Medical Ctr. (Netherlands)
Image Sciences Institute, Univ. Medical Ctr. Utrecht (Netherlands)
Amsterdan Univ. Medical Ctr. (Netherlands)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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