Construction of a 3D probabilistic atlas of human cortical structures

By David W. Shattuck, Mubeena Mirza, Vitria Adisetiyo, Cornelius Hojatkashani, Georges Salamon, Katherine L. Narr, Russell A. Poldrack, Robert M. Bilder, Arthur W. Toga. NeuroImage, 2008

1,202 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e64cc93552ea9fb0e7496380549be5ca62808bc3

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In Cognitive Neuroscience

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Citations in the corpus

Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy (2009, neutral): “LONI Probabilistic Brain Atlas ( Shattuck et al., 2008 ).”
Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images (2012, methodology): “The ROIs are defined by the LONI Probabilistic Brain Atlas (LPBA40) (http://www.loni.ucla. edu/Atlases/LPBA40) (Shattuck et al., 2008), which were generated from anatomic regions of MRI delineated manually from 40 subjects.”
Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019, neutral): “• LPBA (LONI Probabilistic Brain Atlas) [51].”
Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, methodology): “The second test dataset is the LPBA40 dataset [8], which can be downloaded from http://sve.”
Positive effect of acute mild exercise on executive function via arousal-related prefrontal activations: An fNIRS study (2014, methodology): “Based on amethod widely used in anatomical labeling systems, LBPA40 (Shattuck et al., 2008), 3 or 4 neighboring channels were combined to form each ROI.”
101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol (2012, methodology): “Examples of volume-based cortical labeling protocols include those developed at the Center for Morphometric Analysis at the Massachusetts General Hospital (Caviness et al., 1996), the Montreal Neurological Institute (Petrides, 2011), UCLA’s Laboratory of Neuro Imaging (Bilder et al., 2008), as well ”
The SRI24 Multi-Channel Atlas of Normal Adult Human Brain Structure (2009, methodology): “That is not to say that label propagation is impossible with fuzzy atlases, for example, using probabilistic label maps [Mazziotta et al., 2001a; Shattuck et al., 2008].”
CAT: a computational anatomy toolbox for the analysis of structural MRI data (2024, methodology): “For the voxel-based analysis, we used 56 structur es, manuall y labeled in 40 brains that provided the basis for the LPBA40 atlas [ 33 ].”
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks (2020, methodology): “periments Data and Pre-processing We have evaluated our method on brain atlas registration tasks using 425 T1-weighted brain MR scans from the OASIS [16] dataset and 40 brain MR scans from the LPBA40 [22] dataset. In the OASIS dataset, it includes subjects aged from 18 to 96 and 100 of the included ”
Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort (2010, methodology): “To take anatomical variability into account, an important future direction will be to employ a probabilistic atlas, e.g., the Harvard-Oxford atlas (distributed with the FSL software package; http://fsl.fmrib.ox.ac.uk/fsl/), or the LONI probabilistic brain atlas (Shattuck et al., 2008).”
Deep MRI brain extraction: A 3D convolutional neural network for skull stripping (2016, methodology): “The second data set was taken from the LONI Probabilistic Brain Atlas project8 and was entitled LPBA40 (Shattuck et al., 2008).”
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions (2017, neutral): “Bao et al. [41] also presented a multi-scale patch-wise CNN together with dynamic random walker with decay region of interest to obtain smooth segmentation of subcortical structures in IBSR (developed by the Centre for Morphometric Analysis at Massachusetts General Hospital-available at https://www.”
Acute moderate exercise elicits increased dorsolateral prefrontal activation and improves cognitive performance with Stroop test (2010, methodology): “We combined three or four neighboring channels based on a widely used anatomical label, LBPA40 (Shattuck et al., 2008), to form a ROI.”
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable (2011, methodology): “At least for inter-subject registration of MR brain images the necessary labeled data have been provided to the community for years from several sources such as the IBSR [5] and LPBA40 data sets [24], [25].”