List of Citations from Science Citation Index for
C. Xu, D. L. Pham, M. E. Rettmann, D. N. Yu, and J. L. Prince,
"Reconstruction of the Human Cerebral Cortex from Magnetic Resonance
Images," IEEE Transactions on Medical Imaging, 18(6), pp. 467-480, June, 1999.
2000
: 3 2001
: 6 2002
: 8
Total citations: 17
As of October 10, 2002
By Year - By Citations - By Year with Abstract
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2000
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- Thulborn, KR
, and Uttecht, SD
, "Volumetry and topography of the human brain by magnetic
resonance imaging
," INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
, vol. 11
, pp. 198
-208
, 2000
.
Abstract:
Software, termed Morph, is described for the morphometric
analysis of magnetic resonance images of the human brain.
Algorithms for objective contrast border recognition, surface
feature classification, and surface feature contour unfolding
are evaluated. Intraoperator and interoperator variabilities
and errors were determined to be less than 2% over a group of
operators (n = 6) for the known volume of a cerebral hemisphere
obtained at autopsy. Volumetric errors were measured to be +/-
3% for simulated objects and less than 1% for images of
phantoms. Contours of brains of normal elderly subjects (n = 6)
and patients with probable Alzheimer's disease (n = 6),
segmented into sulcal and gyral features to determine
gyrification indices, showed concordance with literature
values. Flat maps or topograms were obtained of the convoluted
cortex by unfolding the segmented contours. The areas of
surface features were readily obtained. The activation of the
frontal eye fields (FEF) defined by functional magnetic
resonance imaging (fMRI) with an oculomotor control task was
mapped onto a topogram of the precentral sulcus. This software
provides accurate volumetric analysis with additional
topographical tools for characterizing convoluted cortical
features and for presenting three-dimensional fMRI activation
patterns as two-dimensional maps. (C) 2001 John Wiley & Sons,
Inc.
- Shattuck, DW
, and Leahy, RM
, "BrainSuite: An automated cortical surface identification tool
," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION -
MICCAI 2000
, LECTURE NOTES IN COMPUTER SCIENCE
, vol. 1935
, pp. 50
-61
, 2000
.
Abstract:
We describe a new magnetic resonance (MR) image analysis tool
that produces cortical surface representations with spherical
topology from MR images of the human brain. The tool provides a
sequence of low-level operations in a single package that can
produce accurate brain segmentations in clinical time. The
tools include skull and scalp removal, image nonuniformity
compensation, voxel-based tissue classification, topological
correction, rendering, and editing functions. The collection of
tools is designed to require minimal user interaction to
produce cortical representations. In this paper we describe the
theory of each stage of the cortical surface identification
process. We then present validation results using real and
phantom data.
- Kochunov, P
, Lancaster, J
, Thompson, P
, Boyer, A
, Hardies, J
, and Fox, P
, "Evaluation of octree regional spatial normalization method for
regional anatomical matching
," HUMAN BRAIN MAPPING
, vol. 11
, pp. 193
-206
, 2000
.
Abstract:
The goal of regional spatial normalization is to remove
anatomical differences between individual three-dimensional
(3D) brain images by warping them to match features of a
standard brain atlas. Processing to fit features at the
limiting resolution of a 3D MR image volume is computationally
intensive, Limiting the broad use of full-resolution regional
spatial normalization. In Kochunov et al. (1999: Neuro-Image
10:724-737), we proposed a regional spatial normalization
algorithm called octree spatial normalization (OSN) that
reduces processing time to minutes while targeting the accuracy
of previous methods. Ln the current study, modifications of the
OSN algorithm for use in human brain images are described and
tested. An automated brain tissue segmentation procedure was
adopted to create anatomical templates to drive feature
matching in white matter, gray matter, and cerebral-spinal
fluid. Three similarity measurement functions (fast-cross
correlation (CC), sum-square error, and centroid) were
evaluated in a group of six subjects. A combination of fast-CC
and centroid was found to provide the best feature matching and
speed. Multiple iterations and multiple applications of the OSN
algorithm were evaluated to improve fit quality. Two
applications of the OSN algorithm with two iterations per
application were found to significantly reduce volumetric
mismatch (up to six times for lateral ventricle) while keeping
processing time under 30 min. The refined version of OSN was
tested with anatomical landmarks from several major sulci in a
group of nine subjects. Anatomical variability was appreciably
reduced for every sulcus investigated, and mean sulcal tracings
accurately followed sulcal tracings in the target brain. Hum.
Brain Mapping 11:193-206, 2000. (C) 2000 Wiley-Liss, Inc.
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2001
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- Shattuck, DW
, and Leahy, RM
, "Automated graph-based analysis and correction of cortical
volume topology
," IEEE TRANSACTIONS ON MEDICAL IMAGING
, vol. 20
, pp. 1167
-1177
, 2001
.
Abstract:
The human cerebral cortex is topologically equivalent to a
sheet and can be considered topologically spherical if it is
closed at the brain stem. Low-level segmentation of magnetic
resonance (MR) imagery typically produces cerebral volumes
whose tessellations are not topologically spherical. We present
a novel algorithm that analyzes and constrains the topology of
a volumetric object. Graphs are formed that represent the
connectivity of voxel segments in the foreground and background
of the image. These graphs are analyzed and minimal corrections
to the volume are made prior to tessellation. We apply the
algorithm to a simple test object and to cerebral white matter
masks generated by a low-level tissue identification sequence.
We tessellate the resulting objects using the marching cubes
algorithm and verify their topology by computing their Euler
characteristics. A key benefit of the algorithm is that it
localizes the change to a volume to the specific areas of its
topological defects.
- Baillard, C
, Hellier, P
, and Barillot, C
, "Segmentation of brain 3D MR images using level sets and dense
registration
," MEDICAL IMAGE ANALYSIS
, vol. 5
, pp. 185
-194
, 2001
.
Abstract:
This paper presents a strategy for the segmentation of brain
from volumetric MR images which integrates 3D segmentation and
3D registration processes. The segmentation process is based on
the level set formalism. A closed 3D surface propagates towards
the desired boundaries through the iterative evolution of a 4D
implicit function. In this work, the propagation relies on a
robust evolution model including adaptive parameters. These
depend on the input data and on statistical distribution
models. The main contribution of this paper is the use of an
automatic registration method to initialize the surface, as an
alternative solution to manual initialization, The registration
is achieved through a robust multiresolution and multigrid
minimization scheme. This coupling significantly improves the
quality of the method, since the segmentation is faster, more
reliable and fully automatic. Quantitative and qualitative
results on both synthetic and real volumetric brain MR images
are presented and discussed. (C) 2001 Elsevier Science B.V. All
rights reserved.
- Rettmann, ME
, Han, X
, and Prince, JL
, "Automated parcellation of the cortical surface for computation
of regional gyrification indices
," NEUROIMAGE
, vol. 13
, pp. S230
-S230
, 2001
.
Abstract:
This paper presents a strategy for the segmentation of brain
from volumetric MR images which integrates 3D segmentation and
3D registration processes. The segmentation process is based on
the level set formalism. A closed 3D surface propagates towards
the desired boundaries through the iterative evolution of a 4D
implicit function. In this work, the propagation relies on a
robust evolution model including adaptive parameters. These
depend on the input data and on statistical distribution
models. The main contribution of this paper is the use of an
automatic registration method to initialize the surface, as an
alternative solution to manual initialization, The registration
is achieved through a robust multiresolution and multigrid
minimization scheme. This coupling significantly improves the
quality of the method, since the segmentation is faster, more
reliable and fully automatic. Quantitative and qualitative
results on both synthetic and real volumetric brain MR images
are presented and discussed. (C) 2001 Elsevier Science B.V. All
rights reserved.
- Tosun, D
, Rettmann, ME
, Tao, XD
, Han, X
, Xu, CY
, and Prince, JL
, "Calculation of human cerebral cortical thickness on opposing
sulcalanks
," NEUROIMAGE
, vol. 13
, pp. S268
-S268
, 2001
.
Abstract:
This paper presents a strategy for the segmentation of brain
from volumetric MR images which integrates 3D segmentation and
3D registration processes. The segmentation process is based on
the level set formalism. A closed 3D surface propagates towards
the desired boundaries through the iterative evolution of a 4D
implicit function. In this work, the propagation relies on a
robust evolution model including adaptive parameters. These
depend on the input data and on statistical distribution
models. The main contribution of this paper is the use of an
automatic registration method to initialize the surface, as an
alternative solution to manual initialization, The registration
is achieved through a robust multiresolution and multigrid
minimization scheme. This coupling significantly improves the
quality of the method, since the segmentation is faster, more
reliable and fully automatic. Quantitative and qualitative
results on both synthetic and real volumetric brain MR images
are presented and discussed. (C) 2001 Elsevier Science B.V. All
rights reserved.
- Fischl, B
, Liu, A
, and Dale, AM
, "Automated manifold surgery: Constructing geometrically accurate
and topologically correct models of the human cerebral cortex
," IEEE TRANSACTIONS ON MEDICAL IMAGING
, vol. 20
, pp. 70
-80
, 2001
.
Abstract:
Highly accurate surface models of the cerebral cortex are
becoming increasingly important as tools in the investigation
of the functional organization of the human brain. The
construction of such models is difficult using current
neuroimaging technology due to the high degree of cortical
folding. Even single voxel misclassifications can result in
erroneous connections being created between adjacent hanks of a
sulcus, resulting in a topologically inaccurate model. These
topological defects cause the cortical model to no longer be
homeomorphic to a sheet, preventing the accurate inflation,
flattening, or spherical morphing of the reconstructed cortex.
Surface deformation techniques can guarantee the topological
correctness of a model, but are time-consuming and may result
in geometrically inaccurate models. in order to address this
need we have developed a technique for taking a model of the
cortex, detecting and fixing the topological defects while
leaving that majority of the model intact, resulting in a
surface that is both geometrically accurate and topologically
correct.
- Ratnanather, JT
, Botteron, KN
, Nishino, T
, Massie, AB
, Lal, RM
, Patel, SG
, Peddi, S
, Todd, RD
, and Miller, MI
, "Validating cortical surface analysis of medial prefrontal
cortex
," NEUROIMAGE
, vol. 14
, pp. 1058
-1069
, 2001
.
Abstract:
This paper describes cortical analysis of 19 high resolution
MRI subvolumes of medial prefrontal cortex (MPFC), a region
that has been implicated in major depressive disorder. An
automated Bayesian segmentation is used to delineate the MRI
subvolumes into cerebrospinal fluid (CSF), gray matter (GM),
white matter (WM), and partial volumes of either CSF/GM or
GM/WM. The intensity value at which there is equal probability
of GM and GM/WM partial volume is used to reconstruct MPFC
cortical surfaces based on a 3-D isocontouring algorithm. The
segmented data and the generated surfaces are validated by
comparison with hand segmented data and semiautomated contours,
respectively. The L-1 distances between Bayesian and hand
segmented data are 0.05-0.10 (n = 5). Fifty percent of the
voxels of the reconstructed surface lie within 0.12-0.28 mm. (n
= 14) from the semiautomated contours. Cortical thickness
metrics are generated in the form of frequency of occurrence
histograms for GM and WM labelled voxels as a function of their
position from the cortical surface. An algorithm to compute the
surface area of the GM/WM interface of the MPFC subvolume is
described. These methods represent a novel approach to
morphometric chacterization of regional cortex features which
may be important in the study of psychiatric disorders such as
major depression. (C) 2001 Academic Press.
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2002
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- Miller, MI
, Trouve, A
, and Younes, L
, "On the metrics and Euler-Lagrange equations of computational
anatomy
," ANNUAL REVIEW OF BIOMEDICAL ENGINEERING
, vol. 4
, pp. 375
-405
, 2002
.
Abstract:
This paper reviews literature, current concepts and approaches
in computational anatomy (CA). The model of CA is a Grenander
deformable template, an orbit generated from a template under
groups of diffeomorphisms. The metric space of all anatomical
images is constructed from the geodesic connecting one
anatomical structure to another in the orbit. The variational
problems specifying these metrics are reviewed along with their
associated Euler-Lagrange equations. The Euler equations of
motion derived by Arnold for the geodesics in the group of
divergence-free volume-preserving diffeomorphisms of
incompressible fluids are generalized for the larger group of
diffeomorphisms used in CA with nonconstant Jacobians. Metrics
that accommodate photometric variation are described extending
the anatomical model to incorporate the construction of
neoplasm. Metrics on landmarked shapes are reviewed as well as
Joshi's diffeomorphism metrics, Bookstein's thin-plate spline
approximate-metrics, and Kendall's affine invariant metrics. We
conclude by showing recent experimental results from the Toga &
Thompson group in growth, the Van Essen group in macaque and
human cortex mapping, and the Csernansky group in hippocampus
mapping for neuropsychiatric studies in aging and
schizophrenia.
- Schmitt, O
, and Bohme, M
, "A robust transcortical profile scanner for generating 2-D
traverses in histological sections of richly curved cortical
courses
," NEUROIMAGE
, vol. 16
, pp. 1103
-1119
, 2002
.
Abstract:
Quantitative analysis of the cerebral cortex has become more
important since neuroimaging methods have revealed many
subfunctions of cortical regions that were thought to be
typical for only one specific function. Furthermore, it is
often unknown if a certain area may be subdivided observer
independently into subareas. These questions lead to an
analytical problem. How can we analyze the cytoarchitecture of
the human cerebral cortex in a quantitative manner in order to
confirm classical transition regions between distinct areas and
to detect new ones. Scanning the cerebral cortex is difficult
because it presents a richly curved course and sectioning
always leads to partially nonperpendicular sectioned regions of
the tissue. Therefore, different methods were tested to
determine which of them are most reliable with respect to
generating perpendicular testlines in the cerebral cortex. We
introduce a new technique based on electrical field theory. The
results of this technique are compared with those of
conventional techniques. It was found that straight traverses
generated by the electrodynamic model present significantly
smaller intertraversal. differences than the conventional
approaches. (C) 2002 Elsevier Science (USA).
- Suri, JS
, Singh, S
, and Reden, L
, "Fusion of region and boundary/surface-based computer vision and
pattern recognition techniques for 2-D and 3-D MR cerebral
cortical segmentation (Part-II): A state-of-the-art review
," PATTERN ANALYSIS AND APPLICATIONS
, vol. 5
, pp. 77
-98
, 2002
.
Abstract:
Extensive growth in functional brain imaging, perfusion-
weighted imaging, diffusion-weighted imaging, brain mapping and
brain scanning techniques has led tremendously to the
importance of cerebral cortical segmentation both in 2-D and 3-
D from volumetric brain magnetic resonance imaging data sets.
Besides that, recent growth in deformable brain segmentation
techniques in 2-D and 3-D has brought the engineering
community, such as the areas of computer vision, image
processing, pattern recognition and graphics, closer to the
medical community, such as to neuro-surgeons, psychiatrists,
oncologists, neuro-radiologists and internists. In Part I of
this research (see Suri et al [1]), an attempt was made to
review the state-of-the-art in 2-D and 3-D cerebral cortical
segmentation techniques from brain magnetic resonance imaging
based on two main classes: region- and boundary/surface-based.
More than 18 different techniques for segmenting the cerebral
cortex from brain slices acquired in orthogonal directions were
shown using region-based techniques. We also showed more than
ten different techniques to segment the cerebral cortex from
magnetic resonance brain volumes using boundary/surface-based
techniques. This paper (Part II) focuses on presenting state-
of-the-art systems based on the fusion of boundary/surface-
based with region-based techniques, also called regional-
geometric deformation models, which takes the paradigm of
partial differential equations in the level set framework. We
also discuss the pros and cons of these various techniques,
besides giving the mathematical foundations for each sub-class
in the cortical taxonomy. Special emphasis is placed on
discussing the advantages, validation, challenges and neuro-
science/clinical applications of cortical segmentation.
- Shattuck, DW
, and Leahy, RM
, "BrainSuite: An automated cortical surface identification tool
," MEDICAL IMAGE ANALYSIS
, vol. 6
, pp. 129
-142
, 2002
.
Abstract:
We describe a new magnetic resonance (MR) image analysis tool
that produces cortical surface representations with spherical
topology from MR images of the human brain. The tool provides a
sequence of low-level operations in a single package that can
produce accurate brain segmentations in clinical time. The
tools include skull and scalp removal, image nonuniformity
compensation, voxel-based tissue classification, topological
correction, rendering, and editing functions. The collection of
tools is designed to require minimal user interaction to
produce cortical representations. In this paper we describe the
theory of each stage of the cortical surface identification
process. We then present classification validation results
using real and phantom data. We also present a study of
interoperator variability. (C) 2002 Published by Elsevier
Science B.V.
- Davatzikos, C
, and Bryan, RN
, "Morphometric analysis of cortical sulci using parametric
ribbons: A study of the central sulcus
," JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY
, vol. 26
, pp. 298
-307
, 2002
.
Abstract:
Interhemispheric and gender differences of the central sulcus
were examined via a parametric ribbon approach. The central
sulcus was found to be deeper and larger in the right
nondominant hemisphere than in the left dominant hemisphere,
both in males and in females. Based on its pattern, that
asymmetry could be attributed to increased connectivity between
motor and somatosensory cortex. facilitating fine movement,
which could constrain the in-depth growth of the central
sulcus. Position asymmetries were also found, which might be
explained by a relative larger parietal association cortex in
men but not in women.
- Rettmann, ME
, Han, X
, Xu, CY
, and Prince, JL
, "Automated sulcal segmentation using watersheds on the cortical
surface
," NEUROIMAGE
, vol. 15
, pp. 329
-344
, 2002
.
Abstract:
The human cortical surface is a highly complex, folded
structure. Sulci, the spaces between the folds, define location
on the cortex and provide a parcellation into anatomically
distinct areas. A topic that has recently received increased
attention is the segmentation of these sulci from magnetic
resonance images, with most work focusing on extracting either
the sulcal spaces between the folds or curve representations of
sulci. Unlike these methods, we propose a technique that
extracts actual regions of the cortical surface that surround
sulci, which we call "sulcal regions." The method is based on a
watershed algorithm applied to a geodesic depth measure on the
cortical surface. A well-known problem with the watershed
algorithm is a tendency toward oversegmentation, meaning that a
single region is segmented as several pieces. To address this
problem, we propose a postprocessing algorithm that merges
appropriate segments from the watershed algorithm. The sulcal
regions are then manually labeled by simply selecting the
appropriate regions with a mouse click and a preliminary study
of sulcal depth is reported. Finally, a scheme is presented for
computing a complete parcellation of the cortical surface. (C)
2002 Elsevier Science.
- Fischl, B
, Salat, DH
, Busa, E
, Albert, M
, Dieterich, M
, Haselgrove, C
, van der Kouwe, A
, Killiany, R
, Kennedy, D
, Klaveness, S
, Montillo, A
, Makris, N
, Rosen, B
, and Dale, AM
, "Whole brain segmentation: Automated labeling of neuroanatomical
structures in the human brain
," NEURON
, vol. 33
, pp. 341
-355
, 2002
.
Abstract:
We present a technique for automatically assigning a
neuroanatomical label to each voxel in an MRI volume based on
probabilistic information automatically estimated from a
manually labeled training set. In contrast to existing
segmentation procedures that only label a small number of
tissue classes, the current method assigns one of 37 labels to
each voxel, including left and right caudate, putamen,
pallidum, thalamus, lateral ventricles, hippocampus, and
amygdala. The classification technique employs a registration
procedure that is robust to anatomical variability, including
the ventricular enlargement typically associated with
neurological diseases and aging. The technique is shown to be
comparable in accuracy to manual labeling, and of sufficient
sensitivity to robustly detect changes in the volume of
noncortical structures that presage the onset of probable
Alzheimer's disease.
- Han, X
, Xu, CY
, Braga-Neto, U
, and Prince, JL
, "Topology correction in brain cortex segmentation using a
multiscale, graph-based algorithm
," IEEE TRANSACTIONS ON MEDICAL IMAGING
, vol. 21
, pp. 109
-121
, 2002
.
Abstract:
Reconstructing an accurate and topologically correct
representation of the cortical surface of the brain is an
important objective in various neuroscience applications. Most
cortical surface reconstruction methods either ignore topology
or correct it using manual editing or methods that lead to
inaccurate reconstructions. Shattuck and Leahy recently
reported a fully automatic method that yields a topologically
correct representation with little distortion of the underlying
segmentation. We provide an alternate approach that has several
advantages over their approach, including the use of arbitrary
digital connectivities, a flexible morphology-based multiscale
approach, and the option of foreground-only or background-only
correction. A detailed analysis of the method's performance on
15 magnetic resonance brain images is provided.
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