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"A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration"

Bob De Vos, Floris F. Berendsen, Max A. Viergever, Hessam Sokooti, Marius Staring and Ivana Išgum

Abstract

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.

 

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Copyright © 2018 by the authors. Published version © 2018 by Elsevier 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

@article{DeVos:2018,
author = "{Bob De Vos and Floris F. Berendsen and Max A. Viergever and Hessam Sokooti and Marius Staring and Ivana Išgum}",
title = "{A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration}",
journal = "{Medical Image Analysis}",
year = "{2018}",
}

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