A fully automatic and robust brain MRI tissue classification method

By Chris A. Cocosco, Alex P. Zijdenbos, Alan C. Evans. Medical Image Anal., 2003

379 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/b9aeb9d457f4c7a2090036eae8ec32738535c117

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In Computer Vision and Pattern Recognition

In Neurology

Citations in the corpus

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification (2007, methodology): “As proposed in Cocosco et al. (2003), we use a transformation to MNI305-space (rigid registration) using the MNI tools.”
White matter lesion extension to automatic brain tissue segmentation on MRI (2009, neutral): “Firstly, we evaluate different atlas registration methods for a brain tissue segmentation method presented by Cocosco et al. (2003) and Vrooman et al. (2007) where atlas registration is used to automatically train a k-nearest neighbor classifier.”
Sexual dimorphism of brain developmental trajectories during childhood and adolescence (2007, neutral): “The registered and corrected volumes were segmented into white matter, gray matter, cerebro-spinal fluid, and background using a neural net classifier (Cocosco et al., 2003; Zijdenbos et al., 2002).”
A Bayesian model for joint segmentation and registration (2006, neutral): “Neuroscientists are keenly interested in automatic methods, which often rely on prior information, to perform this task (Collins et al., 1999; Leventon et al., 2000; Marroquin et al., 2003; Fischl et al., 2004; Pohl et al., 2004a; Ashburner and Friston, 2005).”
A dynamic 4D probabilistic atlas of the developing brain (2011, methodology): “The cerebellum is not shown as it is not present in this slice. significantly different from the average of the population (Cocosco et al., 2003; Pohl et al., 2002).”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “Healthy tissue Van Leemput [76] ICBM No Affine No PA Marroquin [84] ICBM No Affine Level set PA Cocosco [91] 53 manual No Affine No PA Grau [97] 29 manual No No Demons PA Ashburner [114] ICBM No Affine Cosine PA Awate [79] ICBM No Affine No PA Vrooman [92] 22 manual No Affine B-Splines PA Bricq [82]”
Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification (2005, methodology): “This method makes use of stereotaxic spatial priors as well as intensity features to reduce the occurrence of anatomically implausible voxel classification (Cocosco et al., 2003).”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, methodology): “Cocosco et al. [70] developed a method for the robust selection of training samples to make the k NN classification process fully automatic.”
A Generative Model for Image Segmentation Based on Label Fusion (2010, neutral): “Early MR segmentation algorithms mainly dealt with the problem of tissue classification, where local image intensity profiles contain a significant amount of the relevant information [10], [15], [41], [68].”
The NIH MRI study of normal brain development (2006, neutral): “INSECT (Intensity-Normalized Stereotaxic Environment for Classification of Tissues) takes preprocessed input volumes and generates tissue class (gray, white, CSF, lesion subtype) maps using an artificial neural network classifier (Zijdenbos et al., 2002; Cocosco et al., 2003).”
A brain tumor segmentation framework based on outlier detection (2004, methodology): “Cocosco et al. (2003) developed a segmentation method for healthy brains that builds the Minimum Spanning Tree from the training samples and iteratively breaks the edges to remove false positives (pruning).”
Automatic segmentation of MR images of the developing newborn brain (2005, methodology): “Keywords: Automatic brain MRI classification; Automatic brain MRI segmentation; Early brain development; Kernel density estimation; Neonatal MRI; Robust estimation”