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

12 Scopus citations


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

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


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


Dive into the research topics of 'GroupRegNet: A groupwise one-shot deep learning-based 4D image registration method'. Together they form a unique fingerprint.

Cite this