Konzeptionierung eines digitalen webbasierten Antragsportals in der NCTGewebebank Heidelberg

An der NCT-Gewebebank in Heidelberg werden große Mengen menschlichen Gewebes vorgehalten, welche von Forschern hauptsächlich im Rahmen der Tumorforschung verwendet werden können. Da jede Dienstleistung im Zusammenhang mit diesem Gewebe, sowie die Herausgabe dessen zuerst bewilligt werden muss, müssen die forschenden Ärzte und Wissenschaftler einen Antrag an die Gewebebank stellen. Die Antragsstellung erfolgt zum aktuellen Zeitpunkt über ein Online bereit gestelltes Formular, welches im besten Falle digital korrekt ausgefüllt, ausgedruckt und unterschrieben an das Sekretariat der NCT-Gewebebank geschickt wird. Da die Anträge dort wieder von Hand digitalisiert werden, soll der Vorgang online erfolgen und so die Antragsverwaltung erleichtern, sowie weniger anfällig für Fehler machen. Im Rahmen dieser Bachelorarbeit soll ein Konzept entwickelt werden, wie ein Antragsportal funktionieren kann, über das die Forscher online Anträge stellen können. Diese Arbeit ist auf die folgenden Ziele ausgerichtet: - Modellierung des aktuellen Zustands des Prozesses der Antragstellung - Modellierung eines möglichen zukünftigen Prozesses - Vorstellung der möglichen Verbesserungen im Prozess - Konzeptionierung eines Antragsportals - Analyse der Umsetzung dieses Portals mit praktischen Ansätze

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 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

Software Ecosystem Orchestration: The Perspective of Complementors

Software ecosystems (SECOs) driven by platform business models have changed how consumer software is produced and marketed. Also in the enterprise software segment, value networks in the form of SECOs are replacing traditional business models and linear value chains. These SECOs involve three main types of actors: platform sponsor, complementors, and customers. Platform sponsor strategies have been researched broadly, but not the view of complementors. Further, there are few studies of real-world SECOs. In our research, we have investigated the complementor’s perspective on SECOs and their partnership with the platform sponsor. Through exploratory qualitative research using a practical case from the enterprise software industry, we have developed a partner management framework comprising the complementors ’ value creation process, goals, enablers, and instruments. The model can be used generally to gain a better understanding of complementors, and by platform sponsors to improve their partner management processes

Topological Self-Stabilization with Name-Passing Process Calculi

Topological self-stabilization is the ability of a distributed system to have its nodes themselves establish a meaningful overlay network. Independent from the initial network topology, it converges to the desired topology via forwarding, inserting, and deleting links to neighboring nodes. We adapt a linearization algorithm, originally designed for a shared memory model, to asynchronous message-passing. We use an extended localized pi-calculus to model the algorithm and to formally prove its essential self-stabilization properties: closure and weak convergence for every arbitrary initial configuration, and strong convergence for restricted cases

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

Voltage correction factors for air-insulated transmission lines operating in high-Altitude regions to limit corona activity: a review

Nowadays there are several transmission lines projected to be operating in high-altitude regions. It is well known that the installation altitude has an impact on the dielectric behavior of air-insulated systems. As a result, atmospheric and voltage correction factors must be applied in air-insulated transmission systems operating in high-altitude conditions. This paper performs an exhaustiveliteraturereview,includingstate-of-the-artresearchpapersandInternationalStandardsof theavailablecorrectionfactorstolimitcoronaactivityandensureproperperformancewhenplanning air-insulated transmission lines intended for high-altitude areas. It has been found that there are substantial differences among the various correction methods, differences that are more evident at higher altitudes. Most high-voltage standards were not conceived to test samples to be installed in high-altitude regions and, therefore, most high-voltage laboratories are not ready to face this issue, since more detailed information is required. It is proposed to conduct more research on this topic so that the atmospheric corrections and altitude correction factors found in the current International Standards can be updated and/or modified so that high-voltage components to be installed in high-altitude regions can be tested with more accuracy, taking into account their insulation structure.Peer ReviewedPostprint (published version

Meshes Meet Voxels: Abdominal Organ Segmentation via Diffeomorphic Deformations

Abdominal multi-organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. Three-dimensional numeric representations of abdominal shapes are further important for quantitative and statistical analyses thereof. Existing methods in the field, however, are unable to extract highly accurate 3D representations that are smooth, topologically correct, and match points on a template. In this work, we present UNetFlow, a novel diffeomorphic shape deformation approach for abdominal organs. UNetFlow combines the advantages of voxel-based and mesh-based approaches for 3D shape extraction. Our results demonstrate high accuracy with respect to manually annotated CT data and better topological correctness compared to previous methods. In addition, we show the generalization of UNetFlow to MRI.Comment: Preprin

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