GroupRegNet: A groupwise one-shot deep learning-based 4D image registration method

Yunlu Zhang, Xue Wu, H. Michael Gach, Harold Li, Deshan Yang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Accurate deformable four-dimensional (4D) (three-dimensional in space and time) medical images registration is essential in a variety of medical applications. Deep learning-based methods have recently gained popularity in this area for the significantly lower inference time. However, they suffer from drawbacks of non-optimal accuracy and the requirement of a large amount of training data. A new method named GroupRegNet is proposed to address both limitations. The deformation fields to warp all images in the group into a common template is obtained through one-shot learning. The use of the implicit template reduces bias and accumulated error associated with the specified reference image. The one-shot learning strategy is similar to the conventional iterative optimization method but the motion model and parameters are replaced with a convolutional neural network and the weights of the network. GroupRegNet also features a simpler network design and a more straightforward registration process, which eliminates the need to break up the input image into patches. The proposed method was quantitatively evaluated on two public respiratory-binned 4D-computed tomography datasets. The results suggest that GroupRegNet outperforms the latest published deep learning-based methods and is comparable to the top conventional method pTVreg. To facilitate future research, the source code is available at https://github.com/vincentme/GroupRegNet.

Original languageEnglish
Article number045030
JournalPhysics in medicine and biology
Volume66
Issue number4
DOIs
StatePublished - Feb 21 2021

Keywords

  • 4d-ct
  • Deep learning
  • Deformable image registration

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