Nonrigid registration using free-form deformations: application to breast MR images

By Daniel Rueckert, L. Sonoda, C. Hayes, Derek L. G. Hill, Martin O. Leach, D. Hawkes. 1999

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling (2018, methodology): “Then a free-form non-rigid registration with a sparse set of control points was applied (nearest-neighbor interpolation) [16] and fused with a majority voting consensus.”
Adaptive Stochastic Gradient Descent Optimisation for Image Registration (2009, background): “The B-spline transformation (Rueckert et al. 1999) is an example of such a parameterisation.”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “S methods, such as (Heckemann et al., 2006; Rohlng et al., 2004; Wan et al., 2008), relied on 10 nonlinear deformation models, such as spline-based parameterized transformations (Rohde et al., 2003; Rueckert et al., 1999) or non-parametric dieomorphisms (Beg et al., 2005; Vercauteren et al., 2009), ”
Intensity-Based Image Registration by Minimizing Residual Complexity (2010, background): “(j) Local MI result. B-spline control points [ 24 ].”
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable (2011, methodology): “2) Our own implementation [12] (available as part of CMTK [8]) of Rueckert’s free-form deformation (FFD) registration algorithm [13] based on a cubic B-spline transformation model.”
Computer analysis of computed tomography scans of the lung: a survey (2006, methodology): “to the system described in [62], is typical of contemporary volume-based elastic matching algorithms.”
A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations (2001, methodology): “This framework extends and generalizes a previously published method based on free-form deformations (FFDs) using B-splines which was developed for registration of contrast enhanced MR mammography images [1].”
DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting (2009, unknown): “Following standard FFD model, the transformation field is regularized by its ”bending energy” [6,19], and the distance between the control points is chosen at 7 voxels in x − y directions and 2 voxels in z direction.”
Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017, methodology): “For multi-atlas segmentation, we use the same 80 labelled data as atlases, using non-rigid image registration to warp the atlases [13] and cross-correlation as the similarity metric for patch-based label fusion.”
Performing Label-Fusion-Based Segmentation Using Multiple Automatically Generated Templates (2012, methodology): “[2006] where each brain is matched to 30 manually prelabeled templates using a spline-based nonlinear registration technique [Rueckert et al., 1999].”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “on the derivatives of the deformation field formulated as the bending energy have been used for a regular grid of control points in the FFD model [203].”
MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, unknown): “…practice within discrete volumetric approaches (Glocker et al., 2008) to reduce the degrees of freedom by using a multi-resolution series of control point grids within a B-Spline deformation framework, similar to those used by continuous approaches (Rueckert et al., 1999; Andersson et al., 2007).”
Automatic anatomical brain MRI segmentation combining label propagation and decision fusion (2006, methodology): “In this study, we examined the performance and processes of label propagation with decision fusion in MR images of the human brain, starting from detailed expert segmentations of 30 brains and using a registration method based on free-form deformation (Rueckert et al., 1999).”
Deep Learning for Cardiac Image Segmentation: A Review (2019, methodology): “(32) applied a non-rigid image registration method (198) to propagate the labels from the annotated frames to the unlabeled neighboring ones in the cardiac cycle, effectively generating pseudo annotated frames that could be utilized for further training.”
Complementary Function and Integrated Wiring of the Evolutionarily Distinct Drosophila Olfactory Subsystems (2011, methodology): “uk/ dr/software), which uses nonlinear third-order B-spline registration (Rueckert et al., 1999).”
Unsupervised End-to-end Learning for Deformable Medical Image Registration (2017, background): “Though in some cases parametric transformation models (such as rigid, affine and B-Splines transformation) are enough to recover the underlying deformations [33], [38], a more flexible non-parametric transformation model allowing for arbitrary local deformations is usually needed [4], [5].”
4D MR imaging of respiratory organ motion and its variability (2007, methodology): “The algorithm for non-rigid registration that is used in this paper was proposed by Rueckert et al (1999) and Schnabel et al (2003).”