The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration
Author:
Li Meng1, Hu Shunbo1ORCID, Li Guoqiang1, Zhang Fuchun1, Li Jitao1, Yang Yue1, Zhang Lintao1ORCID, Liu Mingtao1, Xu Yan1, Fu Deqian1ORCID, Zhang Wenyin1ORCID, Wang Xing1
Affiliation:
1. School of Information Science and Engineering, Linyi University, Linyi 276000, China
Abstract
Deep-learning-based registration methods can not only save time but also automatically extract deep features from images. In order to obtain better registration performance, many scholars use cascade networks to realize a coarse-to-fine registration progress. However, such cascade networks will increase network parameters by an n-times multiplication factor and entail long training and testing stages. In this paper, we only use a cascade network in the training stage. Unlike others, the role of the second network is to improve the registration performance of the first network and function as an augmented regularization term in the whole process. In the training stage, the mean squared error loss function between the dense deformation field (DDF) with which the second network has been trained and the zero field is added to constrain the learned DDF such that it tends to 0 at each position and to compel the first network to conceive of a better deformation field and improve the network’s registration performance. In the testing stage, only the first network is used to estimate a better DDF; the second network is not used again. The advantages of this kind of design are reflected in two aspects: (1) it retains the good registration performance of the cascade network; (2) it retains the time efficiency of the single network in the testing stage. The experimental results show that the proposed method effectively improves the network’s registration performance compared to other state-of-the-art methods.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference34 articles.
1. Weakly-supervised convolutional neural networks for multimodal image registration;Hu;Med. Image Anal.,2018 2. Shan, S., Yan, W., Guo, X., Chang, E.I., Fan, Y., and Xu, Y. (2017). Unsupervised end-to-end learning for deformable medical image registration. arXiv. 3. BIRNet: Brain image registration using dual-supervised fully convolutional networks;Fan;Med. Image Anal.,2019 4. Deformable image registration using a cue-aware deep regression network;Cao;IEEE Trans. Biomed. Eng.,2018 5. Wang, C., Yang, G., and Papanastasiou, G. (2022). Unsupervised image registration towards enhancing performance and explainability in cardiac and brain image analysis. Sensors, 22.
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