Nonrigid image registration using multi-scale 3D convolutional neural networks

Hessam Sokooti, Bob D. De Vos, Floris Berendsen, Boudewijn Lelieveldt, Ivana Išgum, Marius Staring

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

91 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention - MICCAI 2017
Subtitle of host publication20th International Conference, Proceedings
EditorsM. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. Louis Collins, S. Duchesne
Place of PublicationCham
PublisherSpringer
Pages232-239
Number of pages8
EditionPart 1
ISBN (Electronic)978-3-319-66182-7
ISBN (Print)978-3-319-66181-0
DOIs
Publication statusPublished - 2017
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2017: 20th International Conference - Quebec City, Canada
Duration: 11 Sep 201713 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10433
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceMedical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period11/09/1713/09/17

Keywords

  • Chest CT
  • Convolutional neural networks
  • Image registration
  • Multi-scale analysis

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  • Cite this

    Sokooti, H., De Vos, B. D., Berendsen, F., Lelieveldt, B., Išgum, I., & Staring, M. (2017). Nonrigid image registration using multi-scale 3D convolutional neural networks. In M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, & S. Duchesne (Eds.), Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 : 20th International Conference, Proceedings (Part 1 ed., pp. 232-239). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433). Springer. https://doi.org/10.1007/978-3-319-66182-7_27