Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks
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In this paper we propose a method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. We design a Convolutional Neural Network (CNN) architecture that, in contrast to all other work, directly estimates the displacement vector field (DVF) from a pair of input images. The proposed RegNet is trained using a large set of artificially generated DVFs, does not explicitly define a dissimilarity metric, and integrates image content at multiple scales to equip the network with contextual information. At testing time nonrigid registration is performed in a single shot, in contrast to current iterative methods. We tested RegNet on 3D chest CT follow-up data. The results show that the accuracy of RegNet is on par with a conventional B-spline registration, for anatomy within the capture range. Training RegNet with artificially generated DVFs is therefore a promising approach for obtaining good results on real clinical data, thereby greatly simplifying the training problem. Deformable image registration can therefore be successfully casted as a learning problem.
KeywordsImage registration Convolutional neural networks Multi-scale analysis Chest CT
This work is financed by the Netherlands Organization for Scientific Research (NWO), project 13351. Dr. M.E. Bakker and J. Stolk are acknowledged for providing a ground truth for the SPREAD study data used in this paper. The Tesla K40 used for this research was donated by the NVIDIA Corporation.
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