A deep learning framework for unsupervised affine and deformable image registration

@article{Vos2019ADL,
  title={A deep learning framework for unsupervised affine and deformable image registration},
  author={Bob D. de Vos and Floris F. Berendsen and Max A. Viergever and Hessam Sokooti and Marius Staring and Ivana I{\vs}gum},
  journal={Medical Image Analysis},
  year={2019},
  volume={52},
  pages={128–143}
}
  • Bob D. de Vos, Floris F. Berendsen, +3 authors Ivana Išgum
  • Published in Medical Image Anal. 2019
  • Mathematics, Computer Science, Medicine
  • Medical Image Analysis
  • HighlightsUnsupervised Deep Learning Image Registration (DLIR) is feasible for affine and deformable image registration. [...] Key Method 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.Expand Abstract

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