Unbiased diffeomorphic atlas construction for computational anatomy.

By Sarang C. Joshi, Brad Davis, Matthieu Jomier, Guido Gerig. NeuroImage, 2004

1,009 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/ad7d62a47c7c1a61828112cce4aa9e8566558da9

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In Computer Vision and Pattern Recognition

In Geometry and Topology

Citations in the corpus

A review of atlas-based segmentation for magnetic resonance brain images (2011, background): “Therefore, new groupise registration techniques [34–41] may prove a better way f solving this issue.”
Deformable medical image registration: setting the state of the art with discrete methods. (2011, background): “These problems arise in applications such as matching anatomical MR images to brain atlases in neuroimaging population studies (42,43) or registering 2D images in stacks of microscope images (44).”
The Optimal Template Effect in Hippocampus Studies of Diseased Populations (2009, methodology): “SyGN is a contrast to methods such as congealing (Learned-Miller, 2006) or (Joshi et al., 2004) in that neither method explicitly represents ψ, the geometric component of the template.”
Multi-Atlas Segmentation with Joint Label Fusion (2012, background): “For instance, such errors can be reduced by optimally constructing a single atlas that is the most representative of the population using training data [12], [11], [18].”
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, positive): “Rather than using an arbitrary scan as the reference, it is also possible to learn an unbiased mean volume from all the training images [26].”
Unbiased Average Age-Appropriate Atlases for Pediatric Studies (2010, positive): “As described above, several methods were developed to produce a template more representative of the anatomy (Guimond et al., 1998, 2000, 2001; Mazziotta et al., 2001a,b; Bhatia et al., 2004; Joshi et al., 2004; Lorenzen et al., 2005; Essen and David, 2002; Wang et al., 2005).”
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.”
Medical image registration: a review (2014, background): “…(Huang et al. 2009); surgery simulation (Miller et al. 2010); atlas building and comparison (Freeborough and Fox 1998; Ganser et al. 2004; Joshi et al. 2004; Leow et al. 2006; Wu et al. 2009; Gooya et al. 2011); radiation therapy (Lavely et al. 2004; Foskey et al. 2005); assisted/guided…”
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.”
Fast Predictive Image Registration (2016, methodology): “We created unbiased atlases [7] for 2D and 3D from all the training data using PyCA 3 and use these atlases as our moving image(s).”
Differences in White Matter Fiber Tract Development Present from 6 to 24 Months in Infants with Autism (2012, methodology): “This is followed by an unbiased, deformable atlas building procedure (42) that applies large deformation diffeomorphic metric mapping transformations.”
Population Shape Regression from Random Design Data (2007, background): “where the second term accounts for the noise model of the image (Joshi et al. 2004).”
A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion (2015, positive): “An alternative way for atlas construction is to combine image registration and template estimation together in a Bayesian framework (Joshi et al., 2004; Ma et al., 2008; Van Leemput, 2009; Zhang et al., 2013).”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, methodology): “This is in contrast to methods such as congealing (Learned-Miller, 2006) or (Joshi et al., 2004) in that neither method explicitly optimizes the geometric component of the template.”
Understanding and Interpreting Machine Learning in Medical Image Computing Applications (2018, methodology): “The main consequence of batch effects in prediction studies is that researchers have observed a decrease in classification accuracy on multi-site studies compared with that obtained using a single site [3,12,16].”
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…”