SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration

By K. Marstal, Floris F. Berendsen, Marius Staring, Stefan Klein. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016

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How researchers see this paper

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A deep learning framework for unsupervised affine and deformable image registration (2018, methodology): “SimpleElastix enables integration of Elastix (Klein et al., 2010) in a variety of programming languages.”
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network (2017, methodology): “The conventional registration method (SimpleElastix) was specifically tuned for this task and the DIRNet was not, because it was trained for registration of slices from any timepoint of the cardiac cycle.”
Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019, methodology): “Several initial works shows the possibility of unsupervised learning [19, 20, 38, 50], among which DLIR [20] performs on par with the Bspline method implemented in SimpleElastix [40] (a multilanguage extension of Elastix [35], which is selected as one of our baseline methods).”