In:
Physics in Medicine & Biology, IOP Publishing, Vol. 66, No. 4 ( 2021-02-21), p. 045030-
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 .
Type of Medium:
Online Resource
ISSN:
0031-9155
,
1361-6560
DOI:
10.1088/1361-6560/abd956
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2021
detail.hit.zdb_id:
1473501-5
SSG:
12
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