Geodesic estimation for large deformation anatomical shape averaging and interpolation.

By Brian B. Avants, James C. Gee. NeuroImage, 2004

460 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/07fd17e8153d6e1f5519a9f03662b2becae10ba4

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In Biomedical Engineering

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

A dynamic 4D probabilistic atlas of the developing brain (2011, background): “A similar groupwise approach for atlas construction has also been proposed in a large deformation framework (Avants and Gee, 2004; Joshi et al., 2004), where the average of non-linear transformations is consistently defined even in the presence of large deformations and inverse transformations are…”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, background): “Furthermore, for several command line programs, arguments are not easy to describe in the LONI XML schema (e.g., ANTS – Avants and Gee, 2004).”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, methodology): “…( Davatzikos et al., 2011; Negash et al., 2013; Suk et al., 2015a ) to each subject’s space via HAMMER ( Shen and Davatzikos, 2002 ), although many other advanced registration methods could be also used ( Avants and Gee, 2004 ; Zacharaki et al., 2009 ; Xue et al., 2006 ; Shen et al., 1999 ).”
A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth (2017, background): “A key characteristic of this approach, as compared to the spatiotemporal atlas construction methods recently applied to fetal brain MRI, is the use of diffeomorphic deformations that are smooth and invertible 62, 73 .”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “Nonparametric registration methods, such as [34], [35], [38], [39],”
Shape deformation analysis from the optimal control viewpoint (2014, methodology): “This framework has led to the development of a family of registration algorithms called Large Deformation Diffeomorphic Metric Mapping (LDDMM), in which the correspondence between two shapes comes from the minimization of an objective functional defined as a sum of two terms [6, 7, 19, 22, 23].”
Age-specific CT and MRI templates for spatial normalization (2012, unknown): “A representative scan is shown in Figure 2 (left column) Scans included a bolus of Gd enhancement, which does tend to make blood appear brighter than unenhanced images.”
Automated Volumetry and Regional Thickness Analysis of Hippocampal Subfields and Medial Temporal Cortical Structures in Mild Cognitive Impairment (2014, methodology): “…template T1-weighted MRI from all atlas subjects are used to construct an unbiased population template using the ANTS affine and high-dimensional deformable registration algorithms [Avants et al., 2008] and the diffeomorphic image averaging approach [Avants and Gee, 2004; Joshi et al., 2004].”
Unbiased diffeomorphic atlas construction for computational anatomy. (2004, methodology): “In more recent and related work, Avants and Gee (2004) developed an algorithm in the large deformation diffeomorphic setting for template estimation by averaging velocity fields.”
The Optimal Template Effect in Hippocampus Studies of Diseased Populations (2009, methodology): “This concept of “splitting” the deformation equally between the pair of images is termed pairwise symmetric normalization (SyN) and was introduced in Avants and Gee (2004), further formalized in Avants et al. (2006) and evaluated in Avants et al. (2008) and Klein et al. (2009).”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, unknown): “Similar to Joshi et al. (2004) and Avants and Gee (2004), Lorenzen et al. (2005) applied Fréchet mean estimation to develop a statistical framework for constructing unbiased brain atlases.”
Highly Accurate Inverse Consistent Registration: A Robust Approach (2010, methodology): “Others match at the midpoint (Beg and Kahn, 2007) or warp several inputs to a mean shape (Avants and Gee, 2004).”
Avoiding asymmetry-induced bias in longitudinal image processing (2011, background): “Also see Joshi et al. (2004) and Avants and Gee (2004) for a similar approach in unbiased non-linear atlas creation, warping several images to a mean shape.”
The impact of T1 vs EPI spatial normalization templates for fMRI data analyses (2017, result): “ANTs has been demonstrably more accurate in published studies comparing T1 normalization strategies than standard voxelwise normalization [Avants and Gee, 2004; Avants et al., 2011].”
Within-subject template estimation for unbiased longitudinal image analysis (2012, background): “For example, Joshi et al. (2004), Avants and Gee (2004) or Ashburner (2007) approach unbiased nonlinear atlas construction by iteratively warping several images to a mean.”
Learning Implicit Brain MRI Manifolds with Deep Learning (2018, background): “Many groups have sought to identify the best brain representation, such as building a template or atlas [2], linear mappings [3], Isomap [1], or regressions based on underlying parameters such as size or shape of regions of interest [4].”
A fast diffeomorphic image registration algorithm (2007, background): “A more optimal template would be some form of average (Avants and Gee, 2004; Davis et al., 2004; Lorenzen et al., 2004).”