Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis

By Alex P. Zijdenbos, Reza Forghani, Alan C. Evans. IEEE Transactions on Medical Imaging, 2002

806 citations recorded by the source corpus.

Subfields: Biophysics, Media Technology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/450cc61a3a0604ccc270fb678a2c2744aaceb2f7

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Citations in the corpus

Focal decline of cortical thickness in Alzheimer's disease identified by computational neuroanatomy. (2004, methodology): “The registered and corrected volumes were segmented into white matter, gray matter, cerebrospinal ﬂuid and background using an advanced neural net classiﬁer (Zijdenbos et al. , 2002).”
Brain templates and atlases (2012, methodology): “An automated analysis pipeline (Zijdenbos et al., 2002) also generated gray/white/CSF maps.”
Arterial stiffness, pressure and flow pulsatility and brain structure and function: the Age, Gene/Environment Susceptibility--Reykjavik study. (2011, methodology): “Total brain, white and grey matter, and white matter hyperintensity volumes were computed automatically with an algorithm based on the Montreal Neurological Institute pipeline (Zijdenbos et al., 2002).”
A review of atlas-based segmentation for magnetic resonance brain images (2011, methodology): “Some other methods [52,87–90] directly combine atlas probabilities with other image features such as voxel intensities or spatial coordinates (x) to train a classifier.”
Accelerated Brain Gray Matter Loss in Fibromyalgia Patients: Premature Aging of the Brain? (2007, methodology): “Classification of brain tissue into gray matter, white matter, CSF, and background was performed by means of an advanced neural net classifier (Zijdenbos et al., 2002), labeling each voxel based on the MRI signal (see Fig.”
Sexual dimorphism of brain developmental trajectories during childhood and adolescence (2007, positive): “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).”
Cortical thickness analysis examined through power analysis and a population simulation (2005, neutral): “Each subject's brain is classified into white matter, gray matter, CSF, and background using all available imaging modalities and a classifier trained by stereotaxic space probability maps (Kollokian, 1996; Zijdenbos et al., 2002).”
Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI (2006, methodology): “Cerebral tissue is classified into white matter, gray matter, spinal fluid and background using a neural net classifier (Zijdenbos et al., 2002).”
The NIH MRI study of normal brain development (2006, positive): “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).”
Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. (2007, methodology): “The registered and corrected images were further segmented into gray matter, white matter, cerebrospinal fluid, and background using an advanced neural net classifier (Zijdenbos et al. 2002).”
Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease (2008, methodology): “The registered and corrected images were further segmented into gray matter, white matter, CSF, and background using an advanced neural net classifier (Zijdenbos et al., 2002).”
Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study (2009, methodology): “An advanced neural net classifier was used to segment the registered and corrected volumes into white matter, gray matter, CSF, and background (Zijdenbos et al., 2002).”
Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex (2012, methodology): “The registered and corrected images were further segmented into graymatter, white matter, cerebrospinal fluid and background using a neural net classifier (Tohka et al., 2004; Zijdenbos et al., 2002).”
Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning (2011, methodology): “A neural net classifier (Zijdenbos et al., 2002) was then trained to discriminate between active stain (NeuN, GFAP, or GAP-43), counterstain (4′,6- diamidino-2-phenylindole, DAPI; the counterstain was used to aid in anatomical localization of each slice), and background, and the same classification…”
A Generative Model for Image Segmentation Based on Label Fusion (2010, neutral): “Such supervised segmentation tools are commonly used in many medical imaging applications, including surgical planning [27] and the study of disease progression, aging or healthy development [23], [50], [74].”
Trajectories of cortical thickness maturation in normal brain development – The importance of quality control procedures (2015, positive): “Classify the image intowhitematter, graymatter, cerebrospinalfluid and background using a neural net classifier (INSECT) (Zijdenbos et al., 2002).”