Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network

Zhao, Shengyu; Lau, Tingfung; Luo, Ji; Chang, Eric; Xu, Yan

doi:10.1109/jbhi.2019.2951024

Cited by 243 publications

(203 citation statements)

References 33 publications

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“…The regularization parameter of VoxelMorph is 4. For RCN, we used Volume Tweening Network (VTN) [38] as basic network architecture. RCN also contains four subnetworks: an affine network and three deformable registration subnetworks.…”

Section: F Baseline Methodsmentioning

confidence: 99%

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

2021

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Section: F Baseline Methodsmentioning

confidence: 99%

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

2021

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“…To warp the moving image only once, so as to reduce the influence of multiple interpolations on image quality and registration accuracy, affine transformation parameters are required to calculate the DVF expressed by u a through applying transformation using the parameters on an initial DVF. u a and u d are further aggregated through the way proposed by Zhao et al [28] to yield the final DVF denoted as u f , with which the moving image is warped through a spatial transformer to yield the final registration result. The parameters θ indicate the kernel values of convolutional layers that are updated by iterative optimization.…”

Section: Methodsmentioning

confidence: 99%

ADMIR–Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images

Tang

Tian

et al. 2020

IEEE Access

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We proposed an unsupervised end-to-end Affine and Deformable Medical Image Registration (ADMIR) method based on convolutional neural network (ConvNet). ADMIR includes three key components: an affine registration module for learning the affine transformation parameters, a deformable registration module for learning the displacement vector field, and a spatial transformer for getting the final warped image from both affine and deformable transformation parameters. To evaluate its performance, the magnetic resonance images of drug-addicted brains were used to train and test the model, and we compared it with two state-of-art methods in terms of Dice score, Hausdorff distance, and average symmetric surface distance. The experimental results demonstrated that our proposed ADMIR model outperforms existing methods even with the images without pre-alignment, which suggests that the ADMIR model can be used to achieve quick medical image registration with high accuracy. INDEX TERMS Deformable image registration, affine registration, convolutional neural network, unsupervised learning, end-to-end registration.

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“…Zhou et al [ 110 ] proposed 3D CNN for serial electron microscopy images (experiments were performed on two databases, Cremi and FIB25) registration. Recently, Zhao et al [ 111 ] presented a 3D Volume Tweening Network (VTN) for 3D medical image (liver CT and brain MRI dataset) registration in an unsupervised manner. Compared to the traditional optimization approaches (ANTs [ 107 ], Elastix [ 112 ] and VoxelMorph-2 [ 113 ]), their method was 880 times faster, with state-of-the-art performance.…”

Section: Applications In 3d Medical Imagingmentioning

confidence: 99%

3D Deep Learning on Medical Images: A Review

Singh

Wang

Gupta

et al. 2020

Sensors

389

150

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The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

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Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network

Cited by 243 publications

References 33 publications

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

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

ADMIR–Affine and Deformable Medical Image Registration for Drug-Addicted Brain Images

3D Deep Learning on Medical Images: A Review

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