Preliminary Feasibility Study of Imaging Registration Between Supine and Prone Breast CT in Breast Cancer Radiotherapy Using Residual Recursive Cascaded Networks

Ouyang, Xiaoyun; Liang, Xiaokun; Xie, Yaoqin

doi:10.1109/access.2020.3047829

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Zhu et al [ 13 ] helped a network develop high-similarity spatial correspondence by introducing a local attention model and integrated multi-scale functionality into the attention mechanism module to achieve the coarse-to-fine registration of local information. Ouyang et al [ 14 ] trained their designed subnetworks synergistically by training the residual recursive cascade network to realize cooperation between the subnetworks. Through the connection of the residual network, the registration speed was accelerated.…”

Section: Introductionmentioning

confidence: 99%

The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration

et al. 2023

Sensors

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Optimising inter-patient image registration for image-based data mining in breast radiotherapy

Jaikuna,

Wilson,

Azria

et al. 2024

Physics and Imaging in Radiation Oncology

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Preliminary Feasibility Study of Imaging Registration Between Supine and Prone Breast CT in Breast Cancer Radiotherapy Using Residual Recursive Cascaded Networks

Cited by 2 publications

References 35 publications

The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration

The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration

Optimising inter-patient image registration for image-based data mining in breast radiotherapy

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