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10 March 2020 Mutual information for unsupervised deep learning image registration
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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.
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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bob D. de Vos, Bas H. M. van der Velden, Jörg Sander, Kenneth G. A. Gilhuijs, Marius Staring, and Ivana Išgum "Mutual information for unsupervised deep learning image registration", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130R (10 March 2020); https://doi.org/10.1117/12.2549729
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