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

TL;DR

GroupRegNet introduces a one-shot deep learning approach for 4D medical image registration that improves accuracy, reduces bias, and simplifies the process compared to existing methods, demonstrating superior performance on public datasets.

Contribution

It presents a novel one-shot learning framework with an implicit template for 4D image registration, addressing accuracy and data requirements of prior deep learning methods.

Findings

Outperforms recent deep learning-based registration methods

Comparable to top conventional registration techniques

Reduces bias and error through implicit template use

Abstract

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.