Neurotechnique Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain

By Bruce Fischl, David H. Salat, Evelina Busa, Marilyn S. Albert, M. Dieterich, C. Haselgrove, A. V. D. van der Kouwe, Ronald J. Killiany, Daniel P. Kennedy, Shuna Klaveness and 5 more. 2002

8,769 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2861c24bc829f963a594a5916a9ce9616b6486d7

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Citations in the corpus

Performing Label-Fusion-Based Segmentation Using Multiple Automatically Generated Templates (2012, methodology): “Similarly, our results match or exceed the overlap values presented in other subcortical segmentation techniques [Babalola et al., 2008; Fischl et al., 2002].”
Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI (2009, unknown): “Segmentation using probabilistic information (Fischl et al., 2002, Heckemann et al., 2006) offers more thorough global spatial knowledge compared to single object atlases.”
Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths (2008, background): “The present analysis is limited, however, to the cerebral cortex, and a separate but related set of automated tools is required to generate measurements of the hippocampal formation, amygdala, basal ganglia, and other subcortical structures (Fischl et al., 2002b).”
MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths (2009, unknown): “This suite of methods uses a probabilistic brain atlas of choice, which was initially proposed in 2002 (Fischl et al., 2002), and has undergone several important improvements over the years (Fischl et al.”
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements (2014, methodology): “Representative of the former is the FreeSurfer cortical modeling software package (Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 1999, 2002, 2004)which owes its popularity to public availability, excellent documentation, good performance, and integration with other toolkits, such as the…”
OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019, unknown): “…of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles) 26,27 intensity normalization 35 , tessellation of the gray matter white matter boundary, automated topology correction 25,36 , and surface…”
DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy (2017, methodology): “One of the most commonly used software tools for whole brain segmentation is FreeSurfer (Fischl et al., 2002), which applies an atlas-based segmentation strategy with deformable registration.”
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation (2010, background): “These algorithms range from shape-driven [54], [30], atlas and knowledge-based [35], Markov Random Fields models [10], [31], to classification/learning-based approaches [24].”
MRI Augmentation via Elastic Registration for Brain Lesions Segmentation (2017, methodology): “Well-developed methods for brain tissue segmentation are usually built upon a set of pre-segmented atlases and strict geometrical prior assumptions [8].”
The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements (2012, methodology): “The volume-based stream involves correction for intensity non-uniformity in the MRI data [3], affine transformation to the MNI305 template, intensity normalisation, removal of non-brain tissue [4], linear and non-linear transformations to a probabilistic brain atlas, and finally, labeling of cortica”