2,477 research outputs found
A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale
In this era of complete genomes, our knowledge of neuroanatomical circuitry
remains surprisingly sparse. Such knowledge is however critical both for basic
and clinical research into brain function. Here we advocate for a concerted
effort to fill this gap, through systematic, experimental mapping of neural
circuits at a mesoscopic scale of resolution suitable for comprehensive,
brain-wide coverage, using injections of tracers or viral vectors. We detail
the scientific and medical rationale and briefly review existing knowledge and
experimental techniques. We define a set of desiderata, including brain-wide
coverage; validated and extensible experimental techniques suitable for
standardization and automation; centralized, open access data repository;
compatibility with existing resources, and tractability with current
informatics technology. We discuss a hypothetical but tractable plan for mouse,
additional efforts for the macaque, and technique development for human. We
estimate that the mouse connectivity project could be completed within five
years with a comparatively modest budget.Comment: 41 page
Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes
Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections.
In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art.
The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks.
Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets.
Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy
Inflammation-driven bone formation in a mouse model of ankylosing spondylitis: sequential not parallel processes
Background\ud
\ud
Ankylosing spondylitis (AS) is an immune-mediated arthritis particularly targeting the spine and pelvis and is characterised by inflammation, osteoproliferation and frequently ankylosis. Current treatments that predominately target inflammatory pathways have disappointing efficacy in slowing disease progression. Thus, a better understanding of the causal association and pathological progression from inflammation to bone formation, particularly whether inflammation directly initiates osteoproliferation, is required.\ud
\ud
Methods\ud
\ud
The proteoglycan-induced spondylitis (PGISp) mouse model of AS was used to histopathologically map the progressive axial disease events, assess molecular changes during disease progression and define disease progression using unbiased clustering of semi-quantitative histology. PGISp mice were followed over a 24-week time course. Spinal disease was assessed using a novel semi-quantitative histological scoring system that independently evaluated the breadth of pathological features associated with PGISp axial disease, including inflammation, joint destruction and excessive tissue formation (osteoproliferation). Matrix components were identified using immunohistochemistry.\ud
\ud
Results\ud
\ud
Disease initiated with inflammation at the periphery of the intervertebral disc (IVD) adjacent to the longitudinal ligament, reminiscent of enthesitis, and was associated with upregulated tumor necrosis factor and metalloproteinases. After a lag phase, established inflammation was temporospatially associated with destruction of IVDs, cartilage and bone. At later time points, advanced disease was characterised by substantially reduced inflammation, excessive tissue formation and ectopic chondrocyte expansion. These distinct features differentiated affected mice into early, intermediate and advanced disease stages. Excessive tissue formation was observed in vertebral joints only if the IVD was destroyed as a consequence of the early inflammation. Ectopic excessive tissue was predominantly chondroidal with chondrocyte-like cells embedded within collagen type II- and X-rich matrix. This corresponded with upregulation of mRNA for cartilage markers Col2a1, sox9 and Comp. Osteophytes, though infrequent, were more prevalent in later disease.\ud
\ud
Conclusions\ud
\ud
The inflammation-driven IVD destruction was shown to be a prerequisite for axial disease progression to osteoproliferation in the PGISp mouse. Osteoproliferation led to vertebral body deformity and fusion but was never seen concurrent with persistent inflammation, suggesting a sequential process. The findings support that early intervention with anti-inflammatory therapies will be needed to limit destructive processes and consequently prevent progression of AS
Development of Imaging Mass Spectrometry Analysis of Lipids in Biological and Clinically Relevant Applications
La spectrométrie de masse mesure la masse des ions selon leur rapport masse sur charge. Cette technique est employée dans plusieurs domaines et peut analyser des mélanges complexes. L’imagerie par spectrométrie de masse (Imaging Mass Spectrometry en anglais, IMS), une branche de la spectrométrie de masse, permet l’analyse des ions sur une surface, tout en conservant l’organisation spatiale des ions détectés. Jusqu’à présent, les échantillons les plus étudiés en IMS sont des sections tissulaires végétales ou animales. Parmi les molécules couramment analysées par l’IMS, les lipides ont suscité beaucoup d'intérêt. Les lipides sont impliqués dans les maladies et le fonctionnement normal des cellules; ils forment la membrane cellulaire et ont plusieurs rôles, comme celui de réguler des événements cellulaires. Considérant l’implication des lipides dans la biologie et la capacité du MALDI IMS à les analyser, nous avons développé des stratégies analytiques pour la manipulation des échantillons et l’analyse de larges ensembles de données lipidiques.
La dégradation des lipides est très importante dans l’industrie alimentaire. De la même façon, les lipides des sections tissulaires risquent de se dégrader. Leurs produits de dégradation peuvent donc introduire des artefacts dans l’analyse IMS ainsi que la perte d’espèces lipidiques pouvant nuire à la précision des mesures d’abondance. Puisque les lipides oxydés sont aussi des médiateurs importants dans le développement de plusieurs maladies, leur réelle préservation devient donc critique. Dans les études multi-institutionnelles où les échantillons sont souvent transportés d’un emplacement à l’autre, des protocoles adaptés et validés, et des mesures de dégradation sont nécessaires. Nos principaux résultats sont les suivants : un accroissement en fonction du temps des phospholipides oxydés et des lysophospholipides dans des conditions ambiantes, une diminution de la présence des lipides ayant des acides gras insaturés et un effet inhibitoire sur ses phénomènes de la conservation des sections au froid sous N2. A température et atmosphère ambiantes, les phospholipides sont oxydés sur une échelle de temps typique d’une préparation IMS normale (~30 minutes). Les phospholipides sont aussi décomposés en lysophospholipides sur une échelle de temps de plusieurs jours. La validation d’une méthode de manipulation d’échantillon est d’autant plus importante lorsqu’il s’agit d’analyser un plus grand nombre d’échantillons.
L’athérosclérose est une maladie cardiovasculaire induite par l’accumulation de matériel cellulaire sur la paroi artérielle. Puisque l’athérosclérose est un phénomène en trois dimension (3D), l'IMS 3D en série devient donc utile, d'une part, car elle a la capacité à localiser les molécules sur la longueur totale d’une plaque athéromateuse et, d'autre part, car elle peut identifier des mécanismes moléculaires du développement ou de la rupture des plaques. l'IMS 3D en série fait face à certains défis spécifiques, dont beaucoup se rapportent simplement à la reconstruction en 3D et à l’interprétation de la reconstruction moléculaire en temps réel. En tenant compte de ces objectifs et en utilisant l’IMS des lipides pour l’étude des plaques d’athérosclérose d’une carotide humaine et d’un modèle murin d’athérosclérose, nous avons élaboré des méthodes «open-source» pour la reconstruction des données de l’IMS en 3D. Notre méthodologie fournit un moyen d’obtenir des visualisations de haute qualité et démontre une stratégie pour l’interprétation rapide des données de l’IMS 3D par la segmentation multivariée. L’analyse d’aortes d’un modèle murin a été le point de départ pour le développement des méthodes car ce sont des échantillons mieux contrôlés. En corrélant les données acquises en mode d’ionisation positive et négative, l’IMS en 3D a permis de démontrer une accumulation des phospholipides dans les sinus aortiques. De plus, l’IMS par AgLDI a mis en évidence une localisation différentielle des acides gras libres, du cholestérol, des esters du cholestérol et des triglycérides. La segmentation multivariée des signaux lipidiques suite à l’analyse par IMS d’une carotide humaine démontre une histologie moléculaire corrélée avec le degré de sténose de l’artère. Ces recherches aident à mieux comprendre la complexité biologique de l’athérosclérose et peuvent possiblement prédire le développement de certains cas cliniques.
La métastase au foie du cancer colorectal (Colorectal cancer liver metastasis en anglais, CRCLM) est la maladie métastatique du cancer colorectal primaire, un des cancers le plus fréquent au monde. L’évaluation et le pronostic des tumeurs CRCLM sont effectués avec l’histopathologie avec une marge d’erreur. Nous avons utilisé l’IMS des lipides pour identifier les compartiments histologiques du CRCLM et extraire leurs signatures lipidiques. En exploitant ces signatures moléculaires, nous avons pu déterminer un score histopathologique quantitatif et objectif et qui corrèle avec le pronostic. De plus, par la dissection des signatures lipidiques, nous avons identifié des espèces lipidiques individuelles qui sont discriminants des différentes histologies du CRCLM et qui peuvent potentiellement être utilisées comme des biomarqueurs pour la détermination de la réponse à la thérapie. Plus spécifiquement, nous avons trouvé une série de plasmalogènes et sphingolipides qui permettent de distinguer deux différents types de nécrose (infarct-like necrosis et usual necrosis en anglais, ILN et UN, respectivement). L’ILN est associé avec la réponse aux traitements chimiothérapiques, alors que l’UN est associé au fonctionnement normal de la tumeur.Mass spectrometry is the measurement of the mass over charge ratio of ions. It is broadly applicable and capable of analyzing complex mixtures. Imaging mass spectrometry (IMS) is a branch of mass spectrometry that analyses ions across a surface while conserving their spatial organization on said surface. At this juncture, the most studied IMS samples are thin tissue sections from plants and animals. Among the molecules routinely imaged by IMS, lipids have generated significant interest. Lipids are important in disease and normal cell function as they form cell membranes and act as signaling molecules for cellular events among many other roles. Considering the potential of lipids in biological and clinical applications and the capability of MALDI to ionize lipids, we developed analytical strategies for the handling of samples and analysis of large lipid MALDI IMS datasets.
Lipid degradation is massively important in the food industry with oxidized products producing a bad smell and taste. Similarly, lipids in thin tissue sections cut from whole tissues are subject to degradation, and their degradation products can introduce IMS artifacts and the loss of normally occurring species to degradation can skew accuracy in IMS measures of abundance. Oxidized lipids are also known to be important mediators in the progression of several diseases and their accurate preservation is critical. As IMS studies become multi-institutional and collaborations lead to sample exchange, the need for validated protocols and measures of degradation are necessary. We observed the products of lipid degradation in tissue sections from multiple mouse organs and reported on the conditions promoting and inhibiting their presence as well as the timeline of degradation. Our key findings were the increase in oxidized phospholipids and lysophospholipids from degradation at ambient conditions, the decrease in the presence of lipids containing unsaturations on their fatty acyl chains, and the inhibition of degradation by matrix coating and cold storage of sections under N2 atmosphere. At ambient atmospheric and temperature, lipids degraded into oxidized phospholipids on the time-scale of a normal IMS experiment sample preparation (within 30 min). Lipids then degraded into lysophospholipids’ on a time scale on the order of several days. Validation of sample handling is especially important when a greater number of samples are to be analyzed either through a cohort of samples, or analysis of multiple sections from a single tissue as in serial 3D IMS.
Atherosclerosis is disease caused by accumulation of cellular material at the arterial wall. The accumulation implanted in the cell wall grows and eventually occludes the blood vessel, or causes a stroke. Atherosclerosis is a 3D phenomenon and serial 3D IMS is useful for its ability to localize molecules throughout the length of a plaque and help to define the molecular mechanisms of plaque development and rupture. Serial 3D IMS has many challenges, many of which are simply a matter of producing 3D reconstructions and interpreting them in a timely fashion. In this aim and using analysis of lipids from atherosclerotic plaques from a human carotid and mouse aortic sinuses, we described 3D reconstruction methods using open-source software. Our methodology provides means to obtain high quality visualizations and demonstrates strategies for rapid interpretation of 3D IMS datasets through multivariate segmentation. Mouse aorta from model animals provided a springboard for developing the methods on lower risk samples with less variation with interesting molecular results. 3D MALDI IMS showed localized phospholipid accumulation in the mouse aortic sinuses with correlation between separate positive and negative ionization datasets. Silver-assisted LDI imaging presented differential localization of free fatty acids, cholesterol / cholesterol esters, and triglycerides. The human carotid’s 3D segmentation shows molecular histologies (spatial groupings of imaging pixels with similar spectral fingerprints) correlating to the degree of arterial stenosis. Our results outline the potential for 3D IMS in atherosclerotic research. Molecular histologies and their 3D spatial organization, obtained from the IMS techniques used herein, may predict high-risk features, and particularly identify areas of plaque that have higher-risk of rupture. These investigations would help further unravel the biological complexities of atherosclerosis, and predict clinical outcomes.
Colorectal cancer liver metastasis (CRCLM) is the metastatic disease of primary colorectal cancer, one of the most common cancers worldwide. CRC is a cancer of the endothelial lining of the colon or rectum. CRC itself is often cured with surgery, while CRCLM is more deadly and treated with chemotherapy with more limited efficacy. Prognosticating and assessment of tumors is performed using classical histopathology with a margin of error. We have used lipid IMS to identify the histological compartments and extract their signatures. Using these IMS signatures we obtained a quantitative and objective histopathological score that correlates with prognosis. Additionally, by dissecting out the lipid signatures we have identified single lipid moieties that are unique to different histologies that could potentially be used as new biomarkers for assessing response to therapy. Particularly, we found a series of plasmalogen and sphingolipid species that differentiate infarct-like and usual necrosis, typical of chemotherapeutic response and normal tumor function, respectively
Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases
Postmortem MRI allows brain anatomy to be examined at high resolution and to
link pathology measures with morphometric measurements. However, automated
segmentation methods for brain mapping in postmortem MRI are not well
developed, primarily due to limited availability of labeled datasets, and
heterogeneity in scanner hardware and acquisition protocols. In this work, we
present a high resolution of 135 postmortem human brain tissue specimens imaged
at 0.3 mm isotropic using a T2w sequence on a 7T whole-body MRI scanner.
We developed a deep learning pipeline to segment the cortical mantle by
benchmarking the performance of nine deep neural architectures, followed by
post-hoc topological correction. We then segment four subcortical structures
(caudate, putamen, globus pallidus, and thalamus), white matter
hyperintensities, and the normal appearing white matter. We show generalizing
capabilities across whole brain hemispheres in different specimens, and also on
unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence
at 7T. We then compute localized cortical thickness and volumetric measurements
across key regions, and link them with semi-quantitative neuropathological
ratings. Our code, Jupyter notebooks, and the containerized executables are
publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website:
https://pulkit-khandelwal.github.io/exvivo-brain-upen
Building connectomes using diffusion MRI: why, how and but
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments
A Colour Wheel to Rule them All: Analysing Colour & Geometry in Medical Microscopy
Personalized medicine is a rapidly growing field in healthcare that aims to customize
medical treatments and preventive measures based on each patient’s unique characteristics,
such as their genes, environment, and lifestyle factors. This approach
acknowledges that people with the same medical condition may respond differently
to therapies and seeks to optimize patient outcomes while minimizing the risk
of adverse effects.
To achieve these goals, personalized medicine relies on advanced technologies,
such as genomics, proteomics, metabolomics, and medical imaging. Digital
histopathology, a crucial aspect of medical imaging, provides clinicians with valuable
insights into tissue structure and function at the cellular and molecular levels. By
analyzing small tissue samples obtained through minimally invasive techniques, such
as biopsy or aspirate, doctors can gather extensive data to evaluate potential diagnoses
and clinical decisions. However, digital analysis of histology images presents
unique challenges, including the loss of 3D information and stain variability, which
is further complicated by sample variability. Limited access to data exacerbates
these challenges, making it difficult to develop accurate computational models for
research and clinical use in digital histology.
Deep learning (DL) algorithms have shown significant potential for improving the
accuracy of Computer-Aided Diagnosis (CAD) and personalized treatment models,
particularly in medical microscopy. However, factors such as limited generability,
lack of interpretability, and bias sometimes hinder their clinical impact. Furthermore,
the inherent variability of histology images complicates the development of robust DL
methods. Thus, this thesis focuses on developing new tools to address these issues.
Our essential objective is to create transparent, accessible, and efficient methods
based on classical principles from various disciplines, including histology, medical
imaging, mathematics, and art, to tackle microscopy image registration and colour
analysis successfully. These methods can contribute significantly to the advancement
of personalized medicine, particularly in studying the tumour microenvironment
for diagnosis and therapy research.
First, we introduce a novel automatic method for colour analysis and non-rigid
histology registration, enabling the study of heterogeneity morphology in tumour
biopsies. This method achieves accurate tissue cut registration, drastically reducing
landmark distance and excellent border overlap. Second, we introduce ABANICCO, a novel colour analysis method that combines
geometric analysis, colour theory, fuzzy colour spaces, and multi-label systems
for automatically classifying pixels into a set of conventional colour categories.
ABANICCO outperforms benchmark methods in accuracy and simplicity. It is
computationally straightforward, making it useful in scenarios involving changing
objects, limited data, unclear boundaries, or when users lack prior knowledge of
the image or colour theory. Moreover, results can be modified to match each
particular task.
Third, we apply the acquired knowledge to create a novel pipeline of rigid
histology registration and ABANICCO colour analysis for the in-depth study of
triple-negative breast cancer biopsies. The resulting heterogeneity map and tumour
score provide valuable insights into the composition and behaviour of the tumour,
informing clinical decision-making and guiding treatment strategies.
Finally, we consolidate the developed ideas into an efficient pipeline for tissue
reconstruction and multi-modality data integration on Tuberculosis infection data.
This enables accurate element distribution analysis to understand better interactions
between bacteria, host cells, and the immune system during the course of infection.
The methods proposed in this thesis represent a transparent approach to computational
pathology, addressing the needs of medical microscopy registration and
colour analysis while bridging the gap between clinical practice and computational
research. Moreover, our contributions can help develop and train better, more
robust DL methods.En una época en la que la medicina personalizada está revolucionando la asistencia
sanitaria, cada vez es más importante adaptar los tratamientos y las medidas
preventivas a la composición genética, el entorno y el estilo de vida de cada
paciente. Mediante el empleo de tecnologías avanzadas, como la genómica, la
proteómica, la metabolómica y la imagen médica, la medicina personalizada se
esfuerza por racionalizar el tratamiento para mejorar los resultados y reducir
los efectos secundarios.
La microscopía médica, un aspecto crucial de la medicina personalizada, permite
a los médicos recopilar y analizar grandes cantidades de datos a partir de pequeñas
muestras de tejido. Esto es especialmente relevante en oncología, donde las terapias
contra el cáncer se pueden optimizar en función de la apariencia tisular específica de
cada tumor. La patología computacional, un subcampo de la visión por ordenador,
trata de crear algoritmos para el análisis digital de biopsias. Sin embargo, antes de
que un ordenador pueda analizar imágenes de microscopía médica, hay que seguir
varios pasos para conseguir las imágenes de las muestras.
La primera etapa consiste en recoger y preparar una muestra de tejido del
paciente. Para que esta pueda observarse fácilmente al microscopio, se corta en
secciones ultrafinas. Sin embargo, este delicado procedimiento no está exento de
dificultades. Los frágiles tejidos pueden distorsionarse, desgarrarse o agujerearse,
poniendo en peligro la integridad general de la muestra.
Una vez que el tejido está debidamente preparado, suele tratarse con tintes de
colores característicos. Estos tintes acentúan diferentes tipos de células y tejidos
con colores específicos, lo que facilita a los profesionales médicos la identificación
de características particulares. Sin embargo, esta mejora en visualización tiene
un alto coste. En ocasiones, los tintes pueden dificultar el análisis informático
de las imágenes al mezclarse de forma inadecuada, traspasarse al fondo o alterar
el contraste entre los distintos elementos.
El último paso del proceso consiste en digitalizar la muestra. Se toman imágenes
de alta resolución del tejido con distintos aumentos, lo que permite su análisis por
ordenador. Esta etapa también tiene sus obstáculos. Factores como una calibración
incorrecta de la cámara o unas condiciones de iluminación inadecuadas pueden
distorsionar o hacer borrosas las imágenes. Además, las imágenes de porta completo
obtenidas so de tamaño considerable, complicando aún más el análisis. En general, si bien la preparación, la tinción y la digitalización de las muestras
de microscopía médica son fundamentales para el análisis digital, cada uno de estos
pasos puede introducir retos adicionales que deben abordarse para garantizar un
análisis preciso. Además, convertir un volumen de tejido completo en unas pocas
secciones teñidas reduce drásticamente la información 3D disponible e introduce
una gran incertidumbre.
Las soluciones de aprendizaje profundo (deep learning, DL) son muy prometedoras
en el ámbito de la medicina personalizada, pero su impacto clínico a veces se
ve obstaculizado por factores como la limitada generalizabilidad, el sobreajuste, la
opacidad y la falta de interpretabilidad, además de las preocupaciones éticas y en
algunos casos, los incentivos privados. Por otro lado, la variabilidad de las imágenes
histológicas complica el desarrollo de métodos robustos de DL. Para superar estos
retos, esta tesis presenta una serie de métodos altamente robustos e interpretables
basados en principios clásicos de histología, imagen médica, matemáticas y arte,
para alinear secciones de microscopía y analizar sus colores.
Nuestra primera contribución es ABANICCO, un innovador método de análisis
de color que ofrece una segmentación de colores objectiva y no supervisada y permite
su posterior refinamiento mediante herramientas fáciles de usar. Se ha demostrado
que la precisión y la eficacia de ABANICCO son superiores a las de los métodos
existentes de clasificación y segmentación del color, e incluso destaca en la detección
y segmentación de objetos completos. ABANICCO puede aplicarse a imágenes
de microscopía para detectar áreas teñidas para la cuantificación de biopsias, un
aspecto crucial de la investigación de cáncer.
La segunda contribución es un método automático y no supervisado de segmentación
de tejidos que identifica y elimina el fondo y los artefactos de las
imágenes de microscopía, mejorando así el rendimiento de técnicas más sofisticadas
de análisis de imagen. Este método es robusto frente a diversas imágenes, tinciones
y protocolos de adquisición, y no requiere entrenamiento.
La tercera contribución consiste en el desarrollo de métodos novedosos para
registrar imágenes histopatológicas de forma eficaz, logrando el equilibrio adecuado
entre un registro preciso y la preservación de la morfología local, en función de
la aplicación prevista.
Como cuarta contribución, los tres métodos mencionados se combinan para
crear procedimientos eficientes para la integración completa de datos volumétricos,
creando visualizaciones altamente interpretables de toda la información presente en
secciones consecutivas de biopsia de tejidos. Esta integración de datos puede tener
una gran repercusión en el diagnóstico y el tratamiento de diversas enfermedades,
en particular el cáncer de mama, al permitir la detección precoz, la realización
de pruebas clínicas precisas, la selección eficaz de tratamientos y la mejora en la
comunicación el compromiso con los pacientes. Por último, aplicamos nuestros hallazgos a la integración multimodal de datos y
la reconstrucción de tejidos para el análisis preciso de la distribución de elementos
químicos en tuberculosis, lo que arroja luz sobre las complejas interacciones entre
las bacterias, las células huésped y el sistema inmunitario durante la infección
tuberculosa. Este método también aborda problemas como el daño por adquisición,
típico de muchas modalidades de imagen.
En resumen, esta tesis muestra la aplicación de métodos clásicos de visión por
ordenador en el registro de microscopía médica y el análisis de color para abordar
los retos únicos de este campo, haciendo hincapié en la visualización eficaz y fácil de
datos complejos. Aspiramos a seguir perfeccionando nuestro trabajo con una amplia
validación técnica y un mejor análisis de los datos. Los métodos presentados en esta
tesis se caracterizan por su claridad, accesibilidad, visualización eficaz de los datos,
objetividad y transparencia. Estas características los hacen perfectos para tender
puentes robustos entre los investigadores de inteligencia artificial y los clínicos e
impulsar así la patología computacional en la práctica y la investigación médicas.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretario: Gonzalo Ricardo Ríos Muñoz.- Vocal: Estíbaliz Gómez de Marisca
Geometric representation of neuroanatomical data observed in mouse brain at cellular and gross levels
This dissertation studies two problems related to geometric representation of
neuroanatomical data: (i) spatial representation and organization of individual neurons,
and (ii) reconstruction of three-dimensional neuroanatomical regions from sparse two-dimensional
drawings. This work has been motivated by nearby development of new
technology, Knife-Edge Scanning Microscopy (KESM), that images a whole mouse
brain at cellular level in less than a month.
A method is introduced to represent neuronal data observed in the mammalian brain at
the cellular level using geometric primitives and spatial indexing. A data representation
scheme is defined that captures the geometry of individual neurons using traditional
geometric primitives, points and cross-sectional areas along a trajectory. This
representation captures inferred synapses as directed links between primitives and
spatially indexes observed neurons based on the locations of their cell bodies. This
method provides a set of rules for acquisition, representation, and indexing of KESMgenerated
data.
Neuroanatomical data observed at the gross level provides the underlying regional
framework for neuronal circuits. Accumulated expert knowledge on neuroanatomical organization is usually given as a series of sparse two-dimensional contours. A data
structure and an algorithm are described to reconstruct separating surfaces among
multiple regions from these sparse cross-sectional contours. A topology graph is defined
for each region that describes the topological skeleton of the region’s boundary surface
and that shows between which contours the surface patches should be generated. A
graph-directed triangulation algorithm is provided to reconstruct surface patches
between contours. This graph-directed triangulation algorithm combined together with
a piecewise parametric curve fitting technique ensures that abutting or shared surface
patches are precisely coincident. This method overcomes limitations in i) traditional
surfaces-from-contours algorithms that assume binary, not multiple, regionalization of
space, and in ii) few existing separating surfaces algorithms that assume conversion of
input into a regular volumetric grid, which is not possible with sparse inter-planar
resolution
- …