Average Brain Models: A Convergence Study

By A. Guimond, Jean Meunier, J. Thirion. Computer Vision and Image Understanding, 2000

505 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/195367015c425129c2c5e4c4eabbc5eabf582665

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees (2003, background): “images from different subjects [11] and generating an average atlas [12], [13].”
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles (2019, methodology): “Single-atlas segmentation has been successfully applied to some applications [3-5].”
A dynamic 4D probabilistic atlas of the developing brain (2011, background): “Several different approaches have been explored to address this issue: Guimond et al. (2000) suggest using the inverse of the average non-rigid transformation of each of the subjects to the reference image to find an average reference space.”
White matter hemisphere asymmetries in healthy subjects and in schizophrenia: a diffusion tensor MRI study (2004, methodology): “To address the issue of the blurring, we developed a novel method using a registration scheme with a multiple-channel demons algorithm (Guimond et al., 2000) to make a tensor image symmetric along the interhemispheric fissure, that is, midsagittal plane with reduced blurring effects.”
The Optimal Template Effect in Hippocampus Studies of Diseased Populations (2009, methodology): “Examples of such methods are (Guimond et al., 2000; Twining et al., 2005) and, with diffeomorphisms, (Vaillant et al., 2004; Beg and Khan, 2006; Younes, 2007).”
A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion (2015, unknown): “The update of the reference space and the computation of the template image can be performed iteratively as in (Guimond et al., 2000; Hoogendoorn et al., 2013), TR(k)→i = TR(k−1)→i ◦ (T̄ (k−1))−1 (5) R(k) = 1 N N∑ i=1 Ii(TR(k)→i) (6) Figure 4 shows the template image without registration, after…”
Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression (2012, unknown): “The method presented in Ashburner (2000) and Guimond et al. (2000) endeavors to reduce the bias toward a specific reference by repeating the mapping process of the subjects to a successively updated reference in an iterative manner until the average image converges to a stable atlas.”
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].”
Unbiased diffeomorphic atlas construction for computational anatomy. (2004, methodology): “Previous work (Guimond et al., 2000; Magnotta et al., 2003) has introduced methods for computing approximate inverse consistent registrations by applying inverse consistency constraints on intermediate incremental transformations.”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, unknown): “choice [78].”
Model sparsity and brain pattern interpretation of classification models in neuroimaging (2012, methodology): “Spatial normalization of EPI images was obtained by aligning subject T1s using an iterative process, similar to the method of [59]: (1) register T1 volumes to MNI 152; (2) aver357 age the masked, registered T1 brain volumes; and (3) re-register T1 volumes to the normalized average.”
An unbiased iterative group registration template for cortical surface analysis (2007, methodology): “Other groups have previously explored the principles of groupbased template generation, either for volumetric data (Guimond et al., 2000; Ashburner et al., 2000; Janke et al., 2003) or surface data (Fischl et al.”
A review of atlas-based segmentation for magnetic resonance brain images (2011, background): “[34] A.”
Construction of an abdominal probabilistic atlas and its application in segmentation (2003, background): “of the reference patient can be reduced further [14], [21]; we have not performed such iterations here and leave it to future work.”