524 research outputs found
Konzeptionierung eines digitalen webbasierten Antragsportals in der NCTGewebebank Heidelberg
An der NCT-Gewebebank in Heidelberg werden groĂe Mengen menschlichen Gewebes vorgehalten, welche von Forschern hauptsĂ€chlich im Rahmen der Tumorforschung verwendet werden können. Da jede Dienstleistung im Zusammenhang mit diesem Gewebe, sowie die Herausgabe dessen zuerst bewilligt werden muss, mĂŒssen die forschenden Ărzte und Wissenschaftler einen Antrag an die Gewebebank stellen. Die
Antragsstellung erfolgt zum aktuellen Zeitpunkt ĂŒber ein Online bereit gestelltes Formular, welches im besten Falle digital korrekt ausgefĂŒllt, ausgedruckt und unterschrieben an das Sekretariat der NCT-Gewebebank geschickt wird. Da die AntrĂ€ge dort wieder von Hand digitalisiert werden, soll der Vorgang online erfolgen und so die Antragsverwaltung erleichtern, sowie weniger anfĂ€llig fĂŒr Fehler machen.
Im Rahmen dieser Bachelorarbeit soll ein Konzept entwickelt werden, wie ein Antragsportal funktionieren kann, ĂŒber das die Forscher online AntrĂ€ge stellen können.
Diese Arbeit ist auf die folgenden Ziele ausgerichtet:
- Modellierung des aktuellen Zustands des Prozesses der Antragstellung
- Modellierung eines möglichen zukĂŒnftigen Prozesses
- Vorstellung der möglichen Verbesserungen im Prozess
- Konzeptionierung eines Antragsportals
- Analyse der Umsetzung dieses Portals mit praktischen AnsÀtze
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
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 complementorâs 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 â 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 modiïŹed 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
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