Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

By Daniel Rueckert, Alejandro F Frangi, Julia Anne Schnabel. IEEE Transactions on Medical Imaging, 2003

489 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/18594c2e733dfd7f2aa3c2e40320d5d9b4994806

How researchers see this paper

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

In Biomedical Engineering

1. A. Gholipour
2. Nikos Paragios
3. Aristeidis Sotiras
4. Max A. Viergever
5. Kaundinya S. Gopinath
6. Nicholas Ayache
7. Andreas K. Maier
8. Richard W. Briggs

In Computer Vision and Pattern Recognition

1. Nikos Paragios
2. Aristeidis Sotiras
3. Christos Davatzikos
4. Max A. Viergever
5. Marius Staring
6. Stefan Klein
7. Josien P. W. Pluim
8. Mert Rory Sabuncu

In Geometry and Topology

1. Xavier Pennec
2. Nicholas Ayache
3. Vincent Arsigny
4. Aristeidis Sotiras
5. Nikos Paragios
6. Max A. Viergever
7. Christos Davatzikos
8. Xiahai Zhuang

Citations in the corpus

• Robust Non-rigid Registration Through Agent-Based Action Learning (2017, methodology): “Rueckert et al. [7] proposed to further reduce the dimensionality by constructing an SDM through a principal component analysis (PCA) on the B -spline displacements.”
• A Log-Euclidean Framework for Statistics on Diffeomorphisms (2006, background): “However, Euclidean means of displacement elds do not necessary yield invertible deformations, which makes Euclidean statistics on these parameters (or B-splines as in [ 10 ]) problematic for dieomorphisms.”
• elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “example follow from a statistical deformation model, obtained by principal component analysis [54].”
• Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “rigid transformation models based on basis function interpolators such as TPS [226], and also B-splines in FFD models [203], [216].”
• Deformable Medical Image Registration: A Survey (2013, background): “demonstrated that the model in [174] can encode landmark position information [176].”
• Evaluating Reinforcement Learning Agents for Anatomical Landmark Detection (2019, unknown): “The first category depends on robust rigid or non-rigid image registration techniques to match corresponding points of interest between target and reference images [10, 11].”
• Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images (2016, methodology): “For the detail of SSM construction and the application for shape regularization, readers are referred to the publications from literature [36], [37] The Dice scores of the four different spatial regularization schemes are presented in the sub-table, Spatial Regu, of Table 4.”