Ludwig-Maximilians-Universität München

Digitale Hochschulschriften der LMU
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    20007 research outputs found

    Deep learning for medical image processing

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    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 für viele Klassifikationsalgorithmen entscheidend sein können und somit für klinische Diagnosen von Bedeutung sind. Daher könnten Verbesserungen in der Effizienz und Genauigkeit von Segmentierungsalgorithmen zu erheblichen Fortschritten in der Patientenversorgung und diagnostischen Genauigkeit führen. Deep Learning hat sich in den letzten Jahren als führender Ansatz in verschiedenen Be-reichen der medizinischen Bildverarbeitung etabliert. Die Hauptvorteile dieser Methoden sind Geschwindigkeit und Effizienz, die die klinische Praxis erheblich verändern können. Traditionelle Algorithmen benötigen möglicherweise Stunden, um komplexe Berechnungen durchzuführen, mit Deep Learning können solche rechenintensiven Aufgaben wesentlich schneller, oft innerhalb von Sekunden, ausgeführt werden. Diese Dissertation konzentriert sich auf zwei Segmentierungsstrategien, die voxel- und oberflächenbasierte Segmentierung. Die voxelbasierte Segmentierung weist jedem Voxel eines Bildes ein Klassenlabel zu, während oberflächenbasierte Techniken eine 3D-Oberfläche aus den Eingabebildern rekonstruieren und segmentieren. In dieser Arbeit werden mehrere Methoden für die voxelbasierte Bildsegmentierung vorgestellt. Der Fokus liegt hier auf der Segmentierung von Gehirnstrukturen, Hyperintensitäten der weißen Substanz und abdominellen Organen. Unsere Ansätze begegnen Herausforderungen wie der Anpassung an verschiedene Domänen, dem Lernen mit begrenzten Daten und der Optimierung von Netzwerkarchitekturen, um 3D-Bilder zu verarbeiten. Darüber hinaus werden in dieser Dissertation Möglichkeiten erörtert, mit den Fehlschlägen standardmäßiger Deep-Learning-Ansätze umzugehen, beispielsweise mit seltenen Fällen nach einer Organresektion. Schließlich legen wir den Fokus auf die Rekonstruktion und Parzellierung von kortikalen Oberflächen. Hier wird Deep Learning verwendet, um kortikale Oberflächen aus MRT-Scans als Dreiecksnetz zu extrahieren und diese Oberflächen auf Knoten-Ebene zu parzellieren. Zu den Herausforderungen dieses Ansatzes gehört der Umgang mit unregelmäßigen und topologisch komplexen Strukturen. Diese Arbeit stellt neuartige Deep-Learning-Strategien für die voxel- und oberflächenbasierte medizinische Segmentierung vor. Durch die Bewältigung spezifischer Herausforderungen in jedem Ansatz trägt sie so zur Weiterentwicklung der medizinischen Bildverarbeitung bei

    Einsatz der Magnetresonanztomographie für die Detektion periapikaler Entzündungsprozesse und assoziierter mukosaler Pathologien

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    Causal decomposition of complex systems and prediction of chaos using machine learning

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    We live in a complex system. Therefore, it is essential to possess techniques to analyze and comprehend its intricate dynamics in order to improve decision making. The objective of this dissertation is to contribute to the research that enhances our ability to make these complex systems less intransparent to us. Firstly, we illustrate the impact on practical applications when nonlinearity - an often disregarded factor in causal inference - is taken into account. Therefore, we investigate the causal relationships within these systems, particularly shedding light on the distinction between linear and nonlinear drivers of causality. After developing the necessary methods, we apply them to a real-world use case and demonstrate that making slight adjustments to certain financial market frameworks can result in considerable advantages because of the resolution of the correlation-causation fallacy. Subsequently, once the linear and nonlinear causal connections are understood, we can derive governing equations from the underlying causality structure to enhance the interpretability of models and predictions. By fine-tuning the parameters of these equations through the phenomenon of synchronization of chaos, we can ensure that they optimally represent the data. Nevertheless, not all complex systems can be accurately described by governing equations. Therefore, the implementation of machine learning techniques like reservoir computing in predicting chaotic systems offers significant data-driven advantages. While their architecture is relatively simple, ensuring full interpretability and hardware realizations still relies on increased efficiency and reduced data requirements. This dissertation presents some of the necessary modifications to the traditional reservoir computing architecture to bring physical reservoir computing closer to realization.Wir leben in einem komplexen System. Daher ist es unerlässlich, über Techniken zur Analyse und zum Verständnis seiner verschleierten Dynamik zu verfügen, um die Entscheidungsfindung zu verbessern. Ziel dieser Dissertation ist es, einen Beitrag zur Forschung zu leisten, die unsere Möglichkeiten erweitert, diese komplexen Systeme für uns weniger intransparent zu machen. Zunächst wird aufgezeigt, welche Auswirkungen es auf praktische Anwendungen hat, wenn Nichtlinearität - ein oft vernachlässigter Faktor bei kausaler Inferenz - berücksichtigt wird. Daher untersuchen wir die kausalen Beziehungen innerhalb dieser Systeme und beleuchten insbesondere die Unterscheidung zwischen linearen und nichtlinearen Kausalitätsfaktoren. Nachdem wir die erforderlichen Methoden entwickelt haben, wenden wir sie auf einen realen Anwendungsfall an und zeigen, dass leichte Anpassungen bestimmter Finanzmarktmodelle durch die Auflösung des Korrelations-Kausalitäts-Fehlschlusses zu erheblichen Vorteilen führen können. Sobald die linearen und nichtlinearen Kausalzusammenhänge bekannt sind, können wir aus der zugrunde liegenden Kausalitätsstruktur die Differentialgleichungen ableiten, um die Interpretierbarkeit von Modellierungen und Vorhersagen zu verbessern. Durch die Feinjustierung der Parameter dieser Gleichungen durch das Phänomen der Synchronisierung von Chaos können wir sicherstellen, dass sie die Daten optimal darstellen. Allerdings lassen sich nicht alle komplexen Systeme durch Differentialgleichungen adäquat beschreiben. Daher bietet die Anwendung von Techniken des maschinellen Lernens wie Reservoir Computing bei der Vorhersage chaotischer Systeme erhebliche datenbasierte Vorteile. Obwohl ihre Architektur relativ einfach ist, ist die Gewährleistung einer vollständigen Interpretierbarkeit und Hardware-Realisierung immer noch von einer erhöhten Effizienz und reduzierten Datenanforderungen abhängig. In dieser Dissertation werden einige der notwendigen Änderungen an der traditionellen Architektur vorgestellt, um physikalisches Reservoir Computing näher an die Realisierung zu bringen

    Transformation, deformation, and formation of minerals in the Vredefort and Ries impact structure and implications for magnetic properties of impactites

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    Shock effects of rock-forming minerals with a focus on Fe-Ti-oxides from the Ries- and the Vredefort impact structures were studied in relation to the magnetic properties. Therefore, samples were investigated from locations characterized by enigmatic magnetic anomalies attributed to the respective impact events. The main aim is to gain insights into the host rocks' stress, temperature and oxygen fugacity evolution. Based on different shock effects in impact breccias, the emplacement conditions of the rocks are discussed. Archean basement gneisses within the pronounced magnetic anomaly northwest of the Vredefort impact structure center have quartz (SiO2) grains with shock-generated planar fractures, as documented by two drill cores with ≈10 m depth. Ilmenite (FeTiO3) revealed that shock loading and unloading at relatively low shock pressures (<16 GPa) can result in the formation of mechanical (0001) and {10-11} twins. At re-equilibration temperatures of 600-700°C, exsolution of magnetite (Fe3O4) within ilmenite occurred, forming a few µm-sized magnetite lamellae parallel to the {10-11} twin boundaries and spheroid magnetite along twin and grain boundaries. Furthermore, shearing fractured and locally melted Fe-bearing oxides, which resulted in their intrusion into adjacent shear fractures within neighboring quartz and feldspar. Dauphiné twins associated with shock-induced planar fractures within quartz suggest that the temperatures before the impact event (paleo-depth of 11-23 km resulting in 650-725°C) were higher than the Curie temperature of magnetite (580°C), which is the carrier of the paleomagnetic orientation. Therefore, uplift of the Archean gneiss upon shock-unloading and subsequent cooling in the magnetic field direction present during the Vredefort impact best explains the observed magnetic remanence. The study, furthermore, found no microstructural difference (i.e., phase assemblage, planar fracture abundance and frequency) between samples from the surface and depth of the two drill cores. Lightning strikes heavily influenced the magnetic record of the surficial rocks, however, microstructural products formed from lightning strikes are likely nm-sized and reside below the resolution of the scanning electron microscope. Ilmenite in the Ries impact breccias recorded that at moderate shock pressures (>16 GPa), transformation twin lamellae were generated that share a common {11-20} plane with the host and a 109° angle between the c-axes of host and twin. Moreover, new grains with foam structure formed, which are characterized as orientation domains that also share a common {11-20} plane and whose c�axes span 109° or 99° angles. This crystallographic orientation relationship of new grains and the inferred twins indicates the back-transformation from FeTiO3 high-pressure polymorphs (liuite and wangdaodeite). A variety of different high-temperature reactions generated rutile (TiO2; T=850-1050°C) and minerals of the ferropseudobrookite-armalcolite solid-solution [(Fe,Mg)Ti2O5; T>1140°C] from ilmenite. Furthermore, redox reactions recorded variations in oxygen fugacity. At high temperatures, an enrichment of iron, in terms of elevated Fe/Ti ratios at the rims of ilmenite aggregates, indicates the presence of a reducing agent during the impactite formation, which generated elemental iron. Cooling and subsequent oxidation of iron formed magnetite. Below 700°C at high oxygen fugacity conditions in combination with a leaching agent, pseudorutile (Fe2Ti3O9) was locally created around single ilmenite grains or completely replaced them. A new occurrence of polymict crystalline breccia in the Ries impact structure at the Aumühle quarry exhibits the direct lithological relationship to the underlying Bunte Breccia and overlying suevite. The polymict crystalline breccia consists of ≈50% shocked crystalline clasts from the Variscan basement and ≈50% components from the sedimentary cover sequence, which display no apparent shock effects. Its emplacement likely occurred during the excavation stage of impact cratering. The mathematical Maxwell Z-model describes flow fields during excavation, indicating that shocked material from the crystalline basement was ballistically ejected. A mixture with ballistically ejected sedimentary clasts was subsequently placed on top of Bunte Breccia and then covered by suevite. Reworking of Bunte Breccia and suevite to form polymict crystalline breccia can be excluded based on the absence of glass fragments, larger clast sizes, and random paleomagnetic directions of polymict crystalline breccia compared to suevite. The proposed emplacement is consistent with observations of polymict crystalline breccias from other impact structures. Ballen SiO2 with characteristically curved fractures within impact melt rocks from the Ries impact structure was investigated to elucidate its formation mechanisms and conditions. It likely originated from fluid-inclusion-rich quartz grains in the gneisses of the crystalline basement. Quartz transformed into diaplectic glass upon shock loading, which partly retained structural information about the precursor phase. As a result, the fluid inclusions dissolved into the amorphous phase. Upon shock unloading and subsequent cooling, dehydration caused fracturing of the glass resulting in curved interfaces as similarly observed from volcanic glasses, i.e., perlitic structures. Structural remnants within the diaplectic glass enabled topotactic crystallization, resulting in preferred crystallographic orientations within quartz. In cases without structural information within the amorphous phase, quartz as well as cristobalite (at elevated temperatures) formed with random crystallographic orientations. Dendritic cristobalite only occurs at the rim of the aggregates in correlation with adjacent vesicles and is interpreted to have formed from a fluid-rich melt

    Etablierung von BMP-überexprimierenden humanen mesenchymalen Stammzellen für die zelltherapeutische Anwendung

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    Validierungsstudie zur Differentialdiagonstik von bakterieller Arthritis und Gicht-Arthritis aus Einzelparametern im Gelenkspunktat

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    Synthese hybrider Aminosäure-Nukleotide als Modell für eine RNA-Peptid-Welt

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    Applications of matter reference frames in quantum gravity

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    Geometric flows and the swampland

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    After an introductory chapter on the quantum supersymmetric string, in which particular attention will be devoted to the techniques via which phenomenologically viable models can be obtained from the ultraviolet microscopic degrees of freedom, and a brief review of the swampland program, the technical tools required to deal with geometric flows will be outlined. The evolution of a broad family of scalar and metric bubble solutions under Perelman's combined flow will be then discussed, together with their asymptotic behaviour. Thereafter, the geometric flow equations associated to a generalised version of Perelman's entropy function will be derived and employed in defining the action-induced flow associated to a given theory for a scalar field and a dynamical metric. The problem of preserving Einstein field equations along the corresponding moduli space trajectories will be cured by allowing a supplementary energy-momentum tensor term to appear along the flow. In a particular example, such contribution will be shown to precisely reproduce the infinite tower of states with exponentially dropping masses postulated by the distance conjecture.Nach einer Einführung in den Superstring, in der besonders auf die Methoden eingegangen wird, mit welchen man aus mikroskopischen Freiheitsgraden im ultravioletten Bereich phänomenologisch brauchbare Modelle erhalten kann und einem kurzen Überblick über das Swampland-Programm werden die mathematischen Methoden vorgestellt, die für die Beschreibung von geometrischem Fluss notwendig sind. Danach wird die Entwicklung einer breitgefächerten Familie von skalaren und metrischen Blasenlösungen unter Perelmans kombiniertem Fluss, zusammen mit deren asymptotischen Verhalten diskutiert. Anschließend werden die geometrischen Flussgleichungen, die im Zusammenhang mit einer verallgemeinerten Version der Perelman-Entropiefunktion stehen, hergeleitet und zur Definition des von der Wirkung induzierten Flusses verwendet. Dieser kann mit einer bestimmten Theorie für ein skalares Feld und eine dynamische Metrik in Verbindung gebracht werden. Es wird ein zusätzlicher Energie-Impuls-Tensor eingeführt, so dass während des geometrischen Flusses die Einstein’schen Feldgleichungen entlang der entsprechenden Trajektorie im Modulraum unverändert bleiben. In einem speziellen Beispiel wird gezeigt, dass ein solcher Beitrag einen Turm aus unendlich vielen Zuständen mit exponentiell abfallenden Massen, wie er von der Abstandsvermutung postuliert wird, exakt reproduziert

    Immune cell profiles of tumor and regional lymph nodes in surgically treated non-small cell lung cancer

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    In recent years, improved understanding of the interaction between cancer and the immune system has led to the introduction of various immunotherapy approaches, all harnessing the power of the immune system to impede tumor development, growth and spread. The immune system is a vastly complex network and response to a foreign antigen must be launched in a coordinated fashion both locally as well as systemically. In the case of NSCLC, this requires a response in the lung tissue surrounding the tumor as well as the regional lymph nodes. Most previous studies that attempted to characterize this response were limited by investigating only tumor or lymph nodes, and those who investigated both did not include tumor-free lymph nodes. To tackle these caveats, I have composed a diverse cohort of surgically-treated NSCLC patients containing both long- and short-term survivors. I investigated morphological features in tumor and matched tumor-bearing and non-tumor bearing lymph nodes and analyzed their association with survival. I then used these results to inform transcriptomic analyses of these tissues to determine how morphological changes were reflected on a molecular level. In this thesis, I showed that tumor-infiltrating lymphocytes and macrophages are key components of the immune response in the primary tumor and non-tumor bearing lymph nodes. The importance of lymphocytes in immune mediated tumor control is further corroborated by an association between CD4 expression in non-tumor bearing lymph nodes and survival. Based on these results, immune transcriptomics showed a negative impact of immune dysfunction measured by Tumor Immune Dysfunction and Exclusion (TIDE) score on patient survival. When comparing patients with high levels to patients with low levels of immune dysfunction, CD8 expression was significantly higher in patients with lower levels of immune dysfunction. A more in depth analysis explored the relation of the expression of multiple immune cell exhaustion markers and survival. Among these, TIM-3 and PD-L1 were identified as the only markers to be associated with survival in more than one tissue. Overall, this work presented an integrative approach to assessing immune composition and dysfunction. Levels of immune cell exhaustion markers may indicate a dysfunctional immune status and can predict survival after curative surgery in NSCLC. This work provides the basis for further investigation of the clinical relevance of immune cell exhaustion in early-stage NSCLC

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