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"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

Abstract

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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Copyright © 2017 by the authors. Published version © 2017 by Springer Lecture Notes in Computer Science. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.

 

BibTeX entry

@inproceedings{Sokooti:2017,
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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