Morphological classification of brains via high-dimensional shape transformations and machine learning methods

By Zhiqiang Lao, Dinggang Shen, Zhong Xue, Bilge Karaçali, Susan M. Resnick, Christos Davatzikos. NeuroImage, 2004

355 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/44fe784c92fa8cdd1e85108ec5154d4013f21fb4

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Citations in the corpus

Automatic classification of MR scans in Alzheimer’s disease (2008, background): “One 89 year old AD patient with an MMSE of 29 and one 86 year old control were mis-classified.”
A fast diffeomorphic image registration algorithm (2007, methodology): “In this case, the usefulness of the warping algorithm could be assessed by how well the deformation fields can be used to distinguish between the populations (Lao et al., 2004).”
Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging (2009, neutral): “…the use of multidimensional classification methods, such as support vector machines (SVM) (Burges, 1998; Cristianini and ShaweTaylor, 2000), to assist in the diagnosis of neurological and psychiatric pathologies (Fan et al., 2007, 2008; Kloppel et al., 2008; Lao et al., 2004; Vemuri et al., 2008).”
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies (2008, background): “…their attention toward multivariate analysis and machine learning-based algorithms for distinguishing patients from cognitively normal (CN) subjects (Alexander and Moeller, 1994; Csernansky et al., 2005; Davatzikos et al., 2005; Fan et al., 2005; Freeborough and Fox, 1998b; Lao et al., 2004).”
Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters (2010, neutral): “Lao et al. (2004) tested an SVM-based classi ﬁ cation method by assigning their elderly subjects into one of four age groups and reached an accuracy rate of 90%.”
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements (2007, methodology): “populations can also be estimated from a constructed classifier by a discriminative direction method [11], [15].”
Issues with threshold masking in voxel-based morphometry of atrophied brains (2009, background): “Finally, while not specifically considered here, multivariate machine learning, classification or decoding approaches (Lao et al., 2004; Vemuri et al., 2008; Friston et al., 2008) can also benefit from Issues with masking in VBM of atrophy.”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, methodology): “Theses approaches allow capturing complex multivariate relationships in the data and have been successfully applied to the individual classification of a variety of neurological conditions (Lao et al., 2004; Fan et al., 2007; Klöppel et al., 2008a; Vemuri et al., 2008).”
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008, neutral): “…in the past 5 years as a means to measure subtle and spatially complex imaging patterns that have diagnostic value (Davatzikos et al., 2008a,b; Liu et al., 2004; Lao et al., 2003, 2004; Li et al., 2007; Adeli et al., 2005; Tandon et al., 2006, 2005; Kloppel et al., 2008a; Vemuri et al., 2008).”
Why voxel-based morphometric analysis should be used with great caution when characterizing group differences (2004, methodology): “For example, in Lao et al. (2003), we adopted a nonlinear support vector machine in conjunction with a hierarchical wavelet-based decomposition of the morphological profiles to examine group differences.”
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (2011, methodology): “Also, according to the features being extracted from the structural MRI, the existing classification methods can be roughly divided into three categories, using 1) voxel-wise tissue probability (Fan et al., 2007; Kloppel et al., 2008; Lao et al., 2004; Magnin et al., 2009), 2) cortical thickness (De”
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline (2008, methodology): “This approach has been recently used in several neuroimaging studies and has shown great potential as a diagnostic tool on individuals (Lao et al., 2004; Davatzikos et al., 2005a,b; Fan et al., 2007; Davatzikos et al., in press).”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, background): “been pursued in recent years (Davatzikos, 2004; Duchesne et al., 2006; Fan et al., 2006a, 2007a, 2005, 2006b, 2007b; Golland et al., 2002; Lao et al., 2004; Liu et al., 2004; Timoner et al., 2002), and have shown great potential in a variety of neuroimaging studies (Davatzikos et al.”
Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database (2011, methodology): “This type of approach is used in Lao et al. (2004); Magnin et al. (2009).”