Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops

By Florent Ségonne, J. Pacheco, Bruce Fischl. IEEE Transactions on Medical Imaging, 2007

1,039 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/af2152daa9fdadc48a27a91cea0762360f4fa9c2

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Citations in the corpus

Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “…2011; Zuo and Xing, 2011), 2) brain surface reconstruction via the recon-all command in FREESURFER (Dale et al., 1999; Fischl et al., 1999a,b, 2001; Segonne et al., 2004, 2007), which has been demonstrated to show good test–retest reliability across scanner manufacturers and across field strengths…”
Within-subject template estimation for unbiased longitudinal image analysis (2012, methodology): “…in FreeSurfer starts with the tessellation of the gray matter / white matter boundary, automated topology correction (Fischl et al., 2001; Ségonne et al., 2007), and surface deformation following intensity gradients to optimally place the gray/white and gray/CSF borders at the location…”
Sex differences in the adult human brain: Evidence from 5,216 UK Biobank participants (2017, positive): “3 (http://surfer.nmr.mgh.harvard.edu/; Fischl and Dale 2000; Fischl et al. 2004; Ségonne et al. 2007), according to the Desikan– Killiany atlas (Desikan et al.”
Unique developmental trajectories of cortical thickness and surface area (2014, positive): “…gray matter structures and cerebellar structures (Fischl et al., 2004a,b; Hutton et al., 2009; Salat, 2004), intensity normalization (Sled et al., 1998); tessellation of gray white matter boundary automated topology correction (Fischl et al., 2001; Segonne et al., 2007), and surface deformation.”
Different Partial Volume Correction Methods Lead to Different Conclusions: an 18F-FDG PET Study of Aging (2016, background): “…to provide detailed anatomical analysis for each subject (Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 1999; Segonne et al., 2007) including segmentation of subcortical structures including thalamus, hippocampus, amygdala, and ventricles (Fischl et al.,…”
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature (2010, methodology): “After the topology of this surface was automatically corrected (Segonne et al., 2007), it was inflated in a way that retains much of the shape and metric properties of the original gray–white interface.”
Digital Topology and Geometry in Medical Imaging: A Survey (2015, methodology): “Reconstruction of an accurate and topologically correct representation of an object has been thoroughly studied in the context of cortex segmentation from MRI [9], [33], [40], [238], [346], see Fig.”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, methodology): “FreeSurfer constitutes a suite of automated algorithms for reconstructing accurate surface mesh representations of the cortex from individual subjects’ T1 images (Dale et al., 1999; Fischl et al., 2001; Ségonne et al., 2007).”
Childhood maltreatment is associated with reduced volume in the hippocampal subfields CA3, dentate gyrus, and subiculum (2012, methodology): “Briefly, this process includes motion correction, removal of nonbrain tissue using a hybrid watershed/surface deformation procedure (94), automated Talairach transformation, segmentation of the subcortical white matter and deep GMV structures (93, 96), intensity normalization (99), tessellation of t”
Diffusion tensor imaging shows white matter tracts between human auditory and visual cortex (2011, positive): “…al. 2004), images were intensity corrected and normalized (Sled et al. 1998), subcortical volumetric structures were segmented (Fischl et al. 2002, 2004a) and the grey–white matter boundary was tesselated and topologic inaccuracies automatically corrected (Fischl et al. 2001; Segonne et al. 2007).”
OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease (2019, neutral): “…putamen, ventricles) 26,27 intensity normalization 35 , tessellation of the gray matter white matter boundary, automated topology correction 25,36 , and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location…”
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology (2019, background): “Current standard approaches involve retrospective topology correction [33] of the segmentation in order to ensure this.”
Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness (2017, background): “…intensity normalization (Sled et al., 1998), tessellation of the gray–white matter boundary, automated topology correction (Fischl et al., 2001; Ségonne et al., 2007), and surface deformation following intensity gradients to optimally place the gray–white and gray–CSF borders at the…”
The Human Connectome Project’s Neuroimaging Approach (2016, background): “The structural images also generate geometrical models of brain anatomy (cortical surface meshes and subcortical segmentations 18,21–28 ) that are critical for all other analyses.”