Deformable 2D-3D medical image registration is an essential technique in Computer Integrated Surgery (CIS) to fuse 3D pre-operative data with 2D intra-operative data. Several factors may affect the accuracy of 2D-3D registration, including the number of 2D views, the angle between views, the view angle relative to anatomical objects, the co-registration error between views, the image noise, and...

An image registration technique is presented for the registration of medical images using a hybrid combination of coarse-scale landmark and B-splines deformable registration techniques. The technique is particularly effective for registration problems in which the images to be registered contain large localized deformations. A brief overview of landmark and deformable registration techniques is...

Ths paper examines several applications of deformable registration algorithms in the field of image-guided radiotherapy. The first part focuses on the description of input and output of deformable registration algorithms, with a brief review of conventional and most current methods. The typical applications of deformable registration are then reviewed on the basis of four practical examples. Th...

Multiscale image registration techniques are presented for the registration of medical images using deformable registration models. The techniques are particularly effective for registration problems in which one or both of the images to be registered contains significant levels of noise. A brief overview of existing deformable registration techniques is presented, and experiments using B-splin...

Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the registration pipeline. The upper bound SSD (UB-S...

PURPOSE To measure the sensitivity of deformable image registration to image noise. Deformable image registration can be used to map organ contours and other treatment planning data from one CT to another. These CT studies can be acquired with either conventional fan-beam CT systems or more novel cone-beam CT techniques. However, cone-beam CT images can have higher noise levels than fan-beam CT...

An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to...

Recent deep learning-based methods have shown promising results and runtime advantages in deformable image registration. However, analyzing the effects of hyperparameters searching for optimal regularization parameters prove to be too prohibitive methods. This is because it involves training a substantial number separate models with distinct hyperparameter values. In this paper, we propose cond...

The extraction of quantitative information regarding growth and deformation from series of image data is of significant importance in many fields of science and medicine. Imaging techniques such as MRI, CT and ultrasound provide a means to examine the morphology and in some cases metabolism of tissues. The registration of this image data between different time points after external loading, tre...