A CNN Regression Approach for Real-Time 2D/3D Registration

By Shun Miao, Z. J. Wang, Rui Liao. IEEE Transactions on Medical Imaging, 2016

473 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/816f7455433e83a6365251e743626bb8c8fa17c1

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

An Artificial Agent for Robust Image Registration (2016, methodology): “A CNN-based regression approach was presented in (Miao, Wang, and Liao 2016) to solve 2-”
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network (2017, methodology): “[8] used a convolutional neural network (ConvNet) to predict a transformation matrix for rigid registration of synthetic 2D to 3D images.”
Deep learning in medical image registration: a review (2019, methodology): “References ROI Dimension Patch-based Modality Transformation [107] Implant, TEE 2D-3D Yes Xray Rigid”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, unknown): “In [59], a CNN based regression approach”
Pairwise domain adaptation module for CNN-based 2-D/3-D registration (2018, methodology): “This loss term encourages the PDA module to find a balance between two goals: (1) the real feature is transferred to be as close as possible to the corresponding synthetic feature and (2) the synthetic feature largely remains unchanged.”
Deep Learning Applications in Medical Image Analysis (2018, methodology): “[84] trained a 5 layer CNN on synthetic X-ray images in order to register 3-dimensional models of a knee implant, a hand implant, and a trans-esophageal probe onto 2-dimensional X-ray images, in order to estimate their pose.”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “On the other hand, Miao et al. [178] developed CNN regressors to directly evaluate the registration transformation parameters.”
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, methodology): “Similar to [15], [16], and [21], we propose deep CNN”
MedGAN: Medical Image Translation using GANs (2018, background): “[10], semantic segmentation [11], [12], registration [13] and image enhancement [14]–[16] with the development of deep learning algorithms, especially the convolutional neural network (CNN) [17].”
Slice-to-volume medical image registration: a survey (2017, background): “(Fischer et al., 2015; Miao et al., 2016; Yang et al., 2016) is learned from examples) suggest that these problems are not an exception.”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “Miao et al. 253 used synthetic data only to train a CNN for 2D – 3D image registration.”
Fast Predictive Image Registration (2016, methodology): “Optical ﬂow [16,4] and aﬃne transforms [9] have been computed via deep learning.”
Unsupervised End-to-end Learning for Deformable Medical Image Registration (2017, background): “Miao et al. [20] adopt CNN regressors to directly predict transformation a r X i v : 1711 .”
Deep Learning in Medical Imaging: General Overview (2017, methodology): “Shun Miao et al. (58) proposed a CNN regression approach, referred to as Pose Estimation via Hierarchical Learning (PEHL), to achieve real-time 2D/3D registration, with a large capture range and high accuracy.”
A deep learning framework for unsupervised affine and deformable image registration (2018, methodology): “Training examples for registration have been generated by synthesizing transformation parameters for affine image registration (Miao et al., 2016) and deformable image registration (Eppenhof et al.”