2,471 research outputs found
SubCMap: subject and condition specific effect maps
Current methods for statistical analysis of neuroimaging data identify condition related structural alterations in the human brain by detecting group differences. They construct detailed maps showing population-wide changes due to a condition of interest. Although extremely useful, methods do not provide information on the subject-specific structural alterations and they have limited diagnostic value because group assignments for each subject are required for the analysis. In this article, we propose SubCMap, a novel method to detect subject and condition specific structural alterations. SubCMap is designed to work without the group assignment information in order to provide diagnostic value. Unlike outlier detection methods, SubCMap detections are condition-specific and can be used to study the effects of various conditions or for diagnosing diseases. The method combines techniques from classification, generalization error estimation and image restoration to the identify the condition-related alterations. Experimental evaluation is performed on synthetically generated data as well as data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results on synthetic data demonstrate the advantages of SubCMap compared to population-wide techniques and higher detection accuracy compared to outlier detection. Analysis with the ADNI dataset show that SubCMap detections on cortical thickness data well correlate with non-imaging markers of Alzheimer's Disease (AD), the Mini Mental State Examination Score and Cerebrospinal Fluid amyloid-ÎČ levels, suggesting the proposed method well captures the inter-subject variation of AD effects
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 brainâs
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 brainâs
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 algorithmâs
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 algorithmâs
accuracy.
Finally, both algorithms were tested on a real aging population dataset to prove the
methodsâ 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 GonzĂĄlez de Vega.- Vocal: Gemma Cristina MontĂ© Rubi
V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence
Reconstructing the cortex from longitudinal MRI is indispensable for
analyzing morphological changes in the human brain. Despite the recent
disruption of cortical surface reconstruction with deep learning, challenges
arising from longitudinal data are still persistent. Especially the lack of
strong spatiotemporal point correspondence hinders downstream analyses due to
the introduced noise. To address this issue, we present V2C-Long, the first
dedicated deep learning-based cortex reconstruction method for longitudinal
MRI. In contrast to existing methods, V2C-Long surfaces are directly comparable
in a cross-sectional and longitudinal manner. We establish strong inherent
spatiotemporal correspondences via a novel composition of two deep mesh
deformation networks and fast aggregation of feature-enhanced within-subject
templates. The results on internal and external test data demonstrate that
V2C-Long yields cortical surfaces with improved accuracy and consistency
compared to previous methods. Finally, this improvement manifests in higher
sensitivity to regional cortical atrophy in Alzheimer's disease.Comment: Preprin
Cortical Surface Reconstruction from High-Resolution MR Brain Images
Reconstruction of the cerebral cortex from magnetic resonance (MR) images
is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1âmm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method's performance is illustrated on exvivo images with 0.25â0.35âmm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects
Neural deformation fields for template-based reconstruction of cortical surfaces from MRI
The reconstruction of cortical surfaces is a prerequisite for quantitative
analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing
segmentation-based methods separate the surface registration from the surface
extraction, which is computationally inefficient and prone to distortions. We
introduce Vox2Cortex-Flow (V2C-Flow), a deep mesh-deformation technique that
learns a deformation field from a brain template to the cortical surfaces of an
MRI scan. To this end, we present a geometric neural network that models the
deformation-describing ordinary differential equation in a continuous manner.
The network architecture comprises convolutional and graph-convolutional
layers, which allows it to work with images and meshes at the same time.
V2C-Flow is not only very fast, requiring less than two seconds to infer all
four cortical surfaces, but also establishes vertex-wise correspondences to the
template during reconstruction. In addition, V2C-Flow is the first approach for
cortex reconstruction that models white matter and pial surfaces jointly,
therefore avoiding intersections between them. Our comprehensive experiments on
internal and external test data demonstrate that V2C-Flow results in cortical
surfaces that are state-of-the-art in terms of accuracy. Moreover, we show that
the established correspondences are more consistent than in FreeSurfer and that
they can directly be utilized for cortex parcellation and group analyses of
cortical thickness.Comment: To appear in Medical Image Analysi
Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI
The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains.
In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality.
However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors.
This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation
Deep learning for medical image processing
Medical image segmentation represents a fundamental aspect of medical image computing. It facilitates measurements of anatomical structures, like organ volume and tissue thickness, critical for many classification algorithms which can be instrumental for clinical diagnosis. Consequently, enhancing the efficiency and accuracy of segmentation algorithms could lead to considerable improvements in patient care and diagnostic precision.
In recent years, deep learning has become the state-of-the-art approach in various domains of medical image computing, including medical image segmentation.
The key advantages of deep learning methods are their speed and efficiency, which have the potential to transform clinical practice significantly. Traditional algorithms might require hours to perform complex computations, but with deep learning, such computational tasks can be executed much faster, often within seconds.
This thesis focuses on two distinct segmentation strategies: voxel-based and surface-based.
Voxel-based segmentation assigns a class label to each individual voxel of an image. On the other hand, surface-based segmentation techniques involve reconstructing a 3D surface from the input images, then segmenting that surface into different regions.
This thesis presents multiple methods for voxel-based image segmentation. Here, the focus is segmenting brain structures, white matter hyperintensities, and abdominal organs. Our approaches confront challenges such as domain adaptation, learning with limited data, and optimizing network architectures to handle 3D images. Additionally, the thesis discusses ways to handle the failure cases of standard deep learning approaches, such as dealing with rare cases like patients who have undergone organ resection surgery.
Finally, the thesis turns its attention to cortical surface reconstruction and parcellation. Here, deep learning is used to extract cortical surfaces from MRI scans as triangular meshes and parcellate these surfaces on a vertex level. The challenges posed by this approach include handling irregular and topologically complex structures.
This thesis presents novel deep learning strategies for voxel-based and surface-based medical image segmentation. By addressing specific challenges in each approach, it aims to contribute to the ongoing advancement of medical image computing.Die Segmentierung medizinischer Bilder stellt einen fundamentalen Aspekt der medizinischen Bildverarbeitung dar. Sie erleichtert Messungen anatomischer Strukturen, wie Organvolumen und Gewebedicke, die fĂŒr viele Klassifikationsalgorithmen entscheidend sein können und somit fĂŒr klinische Diagnosen von Bedeutung sind. Daher könnten Verbesserungen in der Effizienz und Genauigkeit von Segmentierungsalgorithmen zu erheblichen Fortschritten in der Patientenversorgung und diagnostischen Genauigkeit fĂŒhren.
Deep Learning hat sich in den letzten Jahren als fĂŒhrender Ansatz in verschiedenen Be-reichen der medizinischen Bildverarbeitung etabliert. Die Hauptvorteile dieser Methoden sind Geschwindigkeit und Effizienz, die die klinische Praxis erheblich verĂ€ndern können. Traditionelle Algorithmen benötigen möglicherweise Stunden, um komplexe Berechnungen durchzufĂŒhren, mit Deep Learning können solche rechenintensiven Aufgaben wesentlich schneller, oft innerhalb von Sekunden, ausgefĂŒhrt werden.
Diese Dissertation konzentriert sich auf zwei Segmentierungsstrategien, die voxel- und oberflÀchenbasierte Segmentierung. Die voxelbasierte Segmentierung weist jedem Voxel eines Bildes ein Klassenlabel zu, wÀhrend oberflÀchenbasierte Techniken eine 3D-OberflÀche aus den Eingabebildern rekonstruieren und segmentieren.
In dieser Arbeit werden mehrere Methoden fĂŒr die voxelbasierte Bildsegmentierung vorgestellt. Der Fokus liegt hier auf der Segmentierung von Gehirnstrukturen, HyperintensitĂ€ten der weiĂen Substanz und abdominellen Organen. Unsere AnsĂ€tze begegnen Herausforderungen wie der Anpassung an verschiedene DomĂ€nen, dem Lernen mit begrenzten Daten und der Optimierung von Netzwerkarchitekturen, um 3D-Bilder zu verarbeiten. DarĂŒber hinaus werden in dieser Dissertation Möglichkeiten erörtert, mit den FehlschlĂ€gen standardmĂ€Ăiger Deep-Learning-AnsĂ€tze umzugehen, beispielsweise mit seltenen FĂ€llen nach einer Organresektion.
SchlieĂlich legen wir den Fokus auf die Rekonstruktion und Parzellierung von kortikalen OberflĂ€chen. Hier wird Deep Learning verwendet, um kortikale OberflĂ€chen aus MRT-Scans als Dreiecksnetz zu extrahieren und diese OberflĂ€chen auf Knoten-Ebene zu parzellieren. Zu den Herausforderungen dieses Ansatzes gehört der Umgang mit unregelmĂ€Ăigen und topologisch komplexen Strukturen.
Diese Arbeit stellt neuartige Deep-Learning-Strategien fĂŒr die voxel- und oberflĂ€chenbasierte medizinische Segmentierung vor. Durch die BewĂ€ltigung spezifischer Herausforderungen in jedem Ansatz trĂ€gt sie so zur Weiterentwicklung der medizinischen Bildverarbeitung bei
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A Novel Approach for the Visualisation and Progression Tracking of Metastatic Bone Disease
Metastatic bone disease (MBD) is a common secondary feature of cancer that can cause significant complications, including severe pain and death. Current methods of diagnosis require a highly trained radiologist capable of interpreting medical images and recognising the sites of MBD. These medical images are often noisy, two dimensional, greyscale and usually have a poor resolution.
In order to help assist with these issues, several studies have shown that computer aided methods can locate MBD within medical images. However these methods are limited in scope, accuracy, sensitivity, explainability and do not improve upon the poor visualisations of the underlying medical imaging data.
To address these limitations, I have developed a novel method of automatic MBD assessment and visualisation using computed tomography (CT) imaging data as the input. The method is fully automated and does not require any human interaction -- although users can interact with a viewer that visualises the results. This method has been tested on CT data from prostate cancer patients as prostate cancer is one of the most common sources of MBD.
The method described in this thesis has a sensitivity of 0.871 when detecting sclerotic and lytic lesions within a single data set. This sensitivity is comparable to existing methods, however the scope in detecting these lesions was limited to the vertebrae in previous studies. My method significantly expands this scope to include the ribs, vertebrae, pelvis and proximal femurs.
The work in this thesis also provides novel visualisations of the disease and does not suffer from explainability issues that plague modern machine learning algorithms.
In addition, I developed a novel method of tracking the spread of MBD at multiple time points using longitudinal CT data. This method is capable of calculating the change in lesion volume size across multiple time points, providing a novel numerical assessment.The Armstrong Trus
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