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Proceedings Paper

Deformable image registration using convolutional neural networks
Author(s): Koen A. J. Eppenhof; Maxime W. Lafarge; Pim Moeskops; Mitko Veta; Josien P. W. Pluim
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Paper Abstract

Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740S (2 March 2018); doi: 10.1117/12.2292443
Show Author Affiliations
Koen A. J. Eppenhof, Eindhoven Univ. of Technology (Netherlands)
Maxime W. Lafarge, Eindhoven Univ. of Technology (Netherlands)
Pim Moeskops, Eindhoven Univ. of Technology (Netherlands)
Mitko Veta, Eindhoven Univ. of Technology (Netherlands)
Josien P. W. Pluim, Eindhoven Univ. of Technology (Netherlands)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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