A review of 3D/2D registration methods for image-guided interventions

By Primoz Markelj, Dejan Tomazevic, Bostjan Likar, Franjo Pernus. Medical Image Anal., 2012

824 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a40384013f0dcb8174353dafad44a97f49c03759

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In Computer Vision and Pattern Recognition

In Radiation

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Review of Point Cloud Registration Algorithms for Mobile Robotics (2015, background): “Although surveys exist to support registration in medical imagery [Maintz and Viergever, 1998, Markelj et al., 2012, Fluck et al., 2011, Pluim et al., 2003] and object reconstructions/recognition [Salvi et al., 2007, Bowyer et al., 2006], there is no scientific publication covering the evolution of…”
Pairwise domain adaptation module for CNN-based 2-D/3-D registration (2018, methodology): “The proposed PDA module is (1) powerful with additional network capacity to model complex domain variances, (2) flexible for different deep learning-based 2-D/3-D registration frameworks, (3) easy to be plugged into any pretrained convolutional neural networks (CNN) model, and (4) trainable with hie”
3D/2D model-to-image registration by imitation learning for cardiac procedures (2018, background): “In the challenging research area of 3D/2D registration, a number of approaches have been proposed [10].”
Slice-to-volume medical image registration: a survey (2017, methodology): “In this review we focus on slice-to-volume registration, while a more comprehensive overview about projective 2D to 3D image registration is presented in the survey by (Markelj et al., 2010).”
A survey of GPU-based medical image computing techniques. (2012, background): “Comparatively, non-rigid registration is utilized when the body parts undergo non-rigidly motions or deform during medical image acquisitions, which is suitable for soft tissues, such as breast and liver (91).”
A survey of medical image registration - under review (2016, positive): “This conclusion is endorsed by the setup of a recent review article on 3D/2D registration methods for image-guided intervention ( Markelj et al., 2012 ), where the classiﬁcation scheme of ( Maintz and Viergever, 1998 ) was adopted with only minor adaptations.”
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, background): “com/SadeghMSalehi/DeepRegistration range from image-based navigation in imaging and imageguided interventions to longitudinal and group analyses [1], [2], [3], [4], [5], [6], [7].”
A CNN Regression Approach for Real-Time 2D/3D Registration (2016, methodology): “In existing methods, accurate 2-D/3-D registration is typically achieved by intensity-based 2-D/3-D registration methods [2]–[5].”
A review of multimodal image matching: Methods and applications (2021, background): “Most works focus on medical image registration to deliver specific instruction, either aiming at 3D– 2D registration [31,32], deformable registration [29], or registration of different objects such as breast [33,34], brain [35], and vascular [36].”