# GroupRegNet: A Groupwise One-shot Deep Learning-based 4D Image Registration Method

arXiv: https://arxiv.org/abs/2009.02613 · HTML: https://mlsanity.com/paper/2009.02613/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI](https://mlsanity.com/paper/2311.01916/) (2023)
- [ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images](https://mlsanity.com/paper/2305.14673/) (2023)
- [Deep-learning-based groupwise registration for motion correction of cardiac T1 mapping](https://mlsanity.com/paper/2406.12456/) (2024)
- [Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer](https://mlsanity.com/paper/2409.11910/) (2024)
- [OncoReg: Medical Image Registration for Oncological Challenges](https://mlsanity.com/paper/2503.23179/) (2025)
- [CMRINet: Joint Groupwise Registration and Segmentation for Cardiac Function Quantification from Cine-MRI](https://mlsanity.com/paper/2505.16452/) (2025)
- [Test Time Optimized Generalized AI-based Medical Image Registration Method](https://mlsanity.com/paper/2512.14556/) (2025)
- [Set-Based Groupwise Registration for Variable-Length, Variable-Contrast Cardiac MRI](https://mlsanity.com/paper/2605.10571/) (2026)
- [Dual-domain U-Nets with embedded back projection operators for motion-resolved 4D CBCT reconstruction](https://mlsanity.com/paper/2608.03430/) (2026)

## Abstract (arXiv)

Accurate deformable 4-dimensional (4D) (3-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 significant 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 (CNN) 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-CT 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.