217 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Surface-Based tools for Characterizing the Human Brain Cortical Morphology
Tesis por compendio de publicacionesThe cortex of the human brain is highly convoluted. These characteristic convolutions
present advantages over lissencephalic brains. For instance, gyrification allows an expansion
of cortical surface area without significantly increasing the cranial volume, thus
facilitating the pass of the head through the birth channel. Studying the human brain’s
cortical morphology and the processes leading to the cortical folds has been critical for an
increased understanding of the pathological processes driving psychiatric disorders such
as schizophrenia, bipolar disorders, autism, or major depression. Furthermore, charting
the normal developmental changes in cortical morphology during adolescence or aging
can be of great importance for detecting deviances that may be precursors for pathology.
However, the exact mechanisms that push cortical folding remain largely unknown.
The accurate characterization of the neurodevelopment processes is challenging. Multiple
mechanisms co-occur at a molecular or cellular level and can only be studied through
the analysis of ex-vivo samples, usually of animal models. Magnetic Resonance Imaging
can partially fill the breach, allowing the portrayal of the macroscopic processes surfacing
on in-vivo samples.
Different metrics have been defined to measure cortical structure to describe the brain’s
morphological changes and infer the associated microstructural events. Metrics such as
cortical thickness, surface area, or cortical volume help establish a relation between the
measured voxels on a magnetic resonance image and the underlying biological processes.
However, the existing methods present limitations or room for improvement.
Methods extracting the lines representing the gyral and sulcal morphology tend to
over- or underestimate the total length. These lines can provide important information
about how sulcal and gyral regions function differently due to their distinctive ontogenesis.
Nevertheless, some methods label every small fold on the cortical surface as a sulcal
fundus, thus losing the perspective of lines that travel through the deeper zones of a sulcal
basin. On the other hand, some methods are too restrictive, labeling sulcal fundi only for
a bunch of primary folds.
To overcome this issue, we have proposed a Laplacian-collapse-based algorithm that
can delineate the lines traversing the top regions of the gyri and the fundi of the sulci
avoiding anastomotic sulci. For this, the cortex, represented as a 3D surface, is segmented
into gyral and sulcal surfaces attending to the curvature and depth at every point
of the mesh. Each resulting surface is spatially filtered, smoothing the boundaries. Then,
a Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the
morphology of each structure. These thin curves are processed to detect where the extremities
or endpoints lie. Finally, sulcal fundi and gyral crown lines are obtained by
eroding the surfaces while preserving the structure topology and connectivity between
the endpoints. The assessment of the presented algorithm showed that the labeled sulcal lines were close to the proposed ground truth length values while crossing through the
deeper (and more curved) regions. The tool also obtained reproducibility scores better or
similar to those of previous algorithms.
A second limitation of the existing metrics concerns the measurement of sulcal width.
This metric, understood as the physical distance between the points on opposite sulcal
banks, can come in handy in detecting cortical flattening or complementing the information
provided by cortical thickness, gyrification index, or such features. Nevertheless,
existing methods only provided averaged measurements for different predefined sulcal
regions, greatly restricting the possibilities of sulcal width and ignoring the intra-region
variability.
Regarding this, we developed a method that estimates the distance from each sulcal
point in the cortex to its corresponding opposite, thus providing a per-vertex map of the
physical sulcal distances. For this, the cortical surface is sampled at different depth levels,
detecting the points where the sulcal banks change. The points corresponding to each sulcal
wall are matched with the closest point on a different one. The distance between those
points is the sulcal width. The algorithm was validated against a simulated sulcus that
resembles a simple fold. Then the tool was used on a real dataset and compared against
two widely-used sulcal width estimation methods, averaging the proposed algorithm’s
values into the same region definition those reference tools use. The resulting values were
similar for the proposed and the reference methods, thus demonstrating the algorithm’s
accuracy.
Finally, both algorithms were tested on a real aging population dataset to prove the
methods’ potential in a use-case scenario. The main idea was to elucidate fine-grained
morphological changes in the human cortex with aging by conducting three analyses: a
comparison of the age-dependencies of cortical thickness in gyral and sulcal lines, an
analysis of how the sulcal and gyral length changes with age, and a vertex-wise study of
sulcal width and cortical thickness.
These analyses showed a general flattening of the cortex with aging, with interesting
findings such as a differential age-dependency of thickness thinning in the sulcal and
gyral regions. By demonstrating that our method can detect this difference, our results
can pave the way for future in vivo studies focusing on macro- and microscopic changes
specific to gyri or sulci. Our method can generate new brain-based biomarkers specific
to sulci and gyri, and these can be used on large samples to establish normative models
to which patients can be compared. In parallel, the vertex-wise analyses show that sulcal
width is very sensitive to changes during aging, independent of cortical thickness. This
corroborates the concept of sulcal width as a metric that explains, in the least, the unique
variance of morphology not fully captured by existing metrics. Our method allows for
sulcal width vertex-wise analyses that were not possible previously, potentially changing
our understanding of how changes in sulcal width shape cortical morphology.
In conclusion, this thesis presents two new tools, open source and publicly available, for estimating cortical surface-based morphometrics. The methods have been validated
and assessed against existing algorithms. They have also been tested on a real dataset,
providing new, exciting insights into cortical morphology and showing their potential for
defining innovative biomarkers.Programa de Doctorado en Ciencia y TecnologĂa BiomĂ©dica por la Universidad Carlos III de MadridPresidente: Juan Domingo Gispert LĂłpez.- Secretario: Norberto Malpica González de Vega.- Vocal: Gemma Cristina MontĂ© Rubi
Statistical Study on Cortical Sulci of Human Brains
Abstract. A method for building a statistical shape model of sulci of the human brain cortex is described. The model includes sulcal fundi that are defined on a spherical map of the cortex. The sulcal fundi are first extracted in a semi-automatic way using an extension of the fast march-ing method. They are then transformed to curves on the unit sphere via a conformal mapping method that maps each cortical point to a point on the unit sphere. The curves that represent sulcal fundi are parameterized with piecewise constant-speed parameterizations. Intermediate points on these curves correspond to sulcal landmarks, which are used to build a point distribution model on the unit sphere. Statistical information of local properties of the sulci, such as curvature and depth, are embedded in the model. Experimental results are presented to show how the models are built.
Cortical Surface Registration and Shape Analysis
A population analysis of human cortical morphometry is critical for insights into brain development or degeneration. Such an analysis allows for investigating sulcal and gyral folding patterns. In general, such a population analysis requires both a well-established cortical correspondence and a well-defined quantification of the cortical morphometry. The highly folded and convoluted structures render a reliable and consistent population analysis challenging. Three key challenges have been identified for such an analysis: 1) consistent sulcal landmark extraction from the cortical surface to guide better cortical correspondence, 2) a correspondence establishment for a reliable and stable population analysis, and 3) quantification of the cortical folding in a more reliable and biologically meaningful fashion. The main focus of this dissertation is to develop a fully automatic pipeline that supports a population analysis of local cortical folding changes. My proposed pipeline consists of three novel components I developed to overcome the challenges in the population analysis: 1) automatic sulcal curve extraction for stable/reliable anatomical landmark selection, 2) group-wise registration for establishing cortical shape correspondence across a population with no template selection bias, and 3) quantification of local cortical folding using a novel cortical-shape-adaptive kernel. To evaluate my methodological contributions, I applied all of them in an application to early postnatal brain development. I studied the human cortical morphological development using the proposed quantification of local cortical folding from neonate age to 1 year and 2 years of age, with quantitative developmental assessments. This study revealed a novel pattern of associations between the cortical gyrification and cognitive development.Doctor of Philosoph
A 3D explainability framework to uncover learning patterns and crucial sub-regions in variable sulci recognition
Precisely identifying sulcal features in brain MRI is made challenging by the
variability of brain folding. This research introduces an innovative 3D
explainability frame-work that validates outputs from deep learning networks in
their ability to detect the paracingulate sulcus, an anatomical feature that
may or may not be present on the frontal medial surface of the human brain.
This study trained and tested two networks, amalgamating local explainability
techniques GradCam and SHAP with a dimensionality reduction method. The
explainability framework provided both localized and global explanations, along
with accuracy of classification results, revealing pertinent sub-regions
contributing to the decision process through a post-fusion transformation of
explanatory and statistical features. Leveraging the TOP-OSLO dataset of MRI
acquired from patients with schizophrenia, greater accuracies of paracingulate
sulcus detection (presence or absence) were found in the left compared to right
hemispheres with distinct, but extensive sub-regions contributing to each
classification outcome. The study also inadvertently highlighted the critical
role of an unbiased annotation protocol in maintaining network performance
fairness. Our proposed method not only offers automated, impartial annotations
of a variable sulcus but also provides insights into the broader anatomical
variations associated with its presence throughout the brain. The adoption of
this methodology holds promise for instigating further explorations and
inquiries in the field of neuroscience
Mindboggling morphometry of human brains
Mindboggle (http://mindboggle.info) is an open source brain morphometry platform that takes in preprocessed T1-weighted MRI data and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. In this article, we document the software and demonstrate its use in studies of shape variation in healthy and diseased humans. The number of different shape measures and the size of the populations make this the largest and most detailed shape analysis of human brains ever conducted. Brain image morphometry shows great potential for providing much-needed biological markers for diagnosing, tracking, and predicting progression of mental health disorders. Very few software algorithms provide more than measures of volume and cortical thickness, while more subtle shape measures may provide more sensitive and specific biomarkers. Mindboggle computes a variety of (primarily surface-based) shapes: area, volume, thickness, curvature, depth, Laplace-Beltrami spectra, Zernike moments, etc. We evaluate Mindboggle’s algorithms using the largest set of manually labeled, publicly available brain images in the world and compare them against state-of-the-art algorithms where they exist. All data, code, and results of these evaluations are publicly available
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