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Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration [PDF]

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp.750-760, 2023, 2023
Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to perform fast end-to-end registration. Many deep registration methods achieved state-of-the-art performance by performing coarse-to-fine registration, where multiple ...
arxiv   +1 more source

ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration [PDF]

, 2022
Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by non-linear transformation, serves as an essential preprocessing step for neuroimaging data. Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the ...
arxiv   +1 more source

A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration [PDF]

Medical Image Analysis, Volume 52, February 2019, Pages 128-143, 2018
Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration.
arxiv   +1 more source

Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images [PDF]

arXiv, 2022
Medical image registration is a challenging task involving the estimation of spatial transformations to establish anatomical correspondence between pairs or groups of images. Recently, deep learning-based image registration methods have been widely explored, and demonstrated to enable fast and accurate image registration in a variety of applications ...
arxiv  

DiffeoRaptor: Diffeomorphic Inter-modal Image Registration using RaPTOR [PDF]

arXiv, 2022
Purpose: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task.
arxiv  

Image Registration Techniques: A Survey [PDF]

, 2017
Image Registration is the process of aligning two or more images of the same scene with reference to a particular image. The images are captured from various sensors at different times and at multiple view-points. Thus to get a better picture of any change of a scene or object over a considerable period of time image registration is important.
arxiv   +1 more source

Deep Learning for Medical Image Registration: A Comprehensive Review [PDF]

International Journal of Computer Information Systems and Industrial Management Applications (ISSN 2150-7988), Volume 14, pp. 173-190, 2022, 2022
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration.
arxiv  

Towards Positive Jacobian: Learn to Postprocess Diffeomorphic Image Registration with Matrix Exponential [PDF]

arXiv, 2022
We present a postprocessing layer for deformable image registration to make a registration field more diffeomorphic by encouraging Jacobians of the transformation to be positive. Diffeomorphic image registration is important for medical imaging studies because of the properties like invertibility, smoothness of the transformation, and topology ...
arxiv  

A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond [PDF]

arXiv, 2023
Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image registration.
arxiv  

Multicenter Assessment of Augmented Reality Registration Methods for Image-guided Interventions [PDF]

arXiv, 2020
Purpose: To evaluate manual and automatic registration times as well as accuracy with augmented reality during alignment of a holographic 3-dimensional (3D) model onto the real-world environment. Method: 18 participants in various stages of clinical training across two academic centers registered a 3D CT phantom model onto a CT grid using the ...
arxiv