"Nonrigid Image Registration Using Multi-Scale 3D Convolutional Neural Networks"

Hessam Sokooti, Bob de Vos, Floris Berendsen, Boudewijn P.F. Lelieveldt, Ivana Išgum and Marius Staring


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.



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BibTeX entry

author = "{Hessam Sokooti and Bob de Vos and Floris Berendsen and Boudewijn P.F. Lelieveldt and Ivana Išgum and Marius Staring}",
title = "{Nonrigid Image Registration Using Multi-Scale 3D Convolutional Neural Networks}",
booktitle = "{Medical Image Computing and Computer-Assisted Intervention}",
editor = "{Maxime Descoteaux and Lena Maier-Hein and Alfred Franz and Pierre Jannin and D. Louis Collins and Simon Duchesne}",
address = "{Quebec,Canada}",
series = "{Lecture Notes in Computer Science}",
volume = "{10433}",
pages = "{232 - 239}",
month = "{September}",
year = "{2017}",

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