793 research outputs found
Integrating multi-type aberrations from DNA and RNA through dynamic mapping gene space for subtype-specific breast cancer driver discovery
Driver event discovery is a crucial demand for breast cancer diagnosis and
therapy. Especially, discovering subtype-specificity of drivers can prompt the
personalized biomarker discovery and precision treatment of cancer patients.
still, most of the existing computational driver discovery studies mainly
exploit the information from DNA aberrations and gene interactions. Notably,
cancer driver events would occur due to not only DNA aberrations but also RNA
alternations, but integrating multi-type aberrations from both DNA and RNA is
still a challenging task for breast cancer drivers. On the one hand, the data
formats of different aberration types also differ from each other, known as
data format incompatibility. One the other hand, different types of aberrations
demonstrate distinct patterns across samples, known as aberration type
heterogeneity. To promote the integrated analysis of subtype-specific breast
cancer drivers, we design a "splicing-and-fusing" framework to address the
issues of data format incompatibility and aberration type heterogeneity
respectively. To overcome the data format incompatibility, the "splicing-step"
employs a knowledge graph structure to connect multi-type aberrations from the
DNA and RNA data into a unified formation. To tackle the aberration type
heterogeneity, the "fusing-step" adopts a dynamic mapping gene space
integration approach to represent the multi-type information by vectorized
profiles. The experiments also demonstrate the advantages of our approach in
both the integration of multi-type aberrations from DNA and RNA and the
discovery of subtype-specific breast cancer drivers. In summary, our
"splicing-and-fusing" framework with knowledge graph connection and dynamic
mapping gene space fusion of multi-type aberrations data from DNA and RNA can
successfully discover potential breast cancer drivers with subtype-specificity
indication.Comment: 14 pages, 5 figures, 1 tabl
Illustrative Flow Visualization of 4D PC-MRI Blood Flow and CFD Data
Das zentrale Thema dieser Dissertation ist die Anwendung illustrativer Methoden auf zwei bisher ungelöste Probleme der Strömungsvisualisierung. Das Ziel der Strömungsvisualisierung ist die Bereitstellung von Software, die Experten beim Auswerten ihrer Strömungsdaten und damit beim Erkenntnisgewinn unterstützt. Bei der illustrativen Visualisierung handelt es sich um einen Zweig der Visualisierung, der sich an der künstlerischen Arbeit von Illustratoren orientiert. Letztere sind darauf spezialisiert komplizierte Zusammenhänge verständlich und ansprechend zu vermitteln. Die angewendeten Techniken werden in der illustrativen Visualisierung auf reale Daten übertragen, um die Effektivität der Darstellung zu erhöhen. Das erste Problem, das im Rahmen dieser Dissertation bearbeitet wurde, ist die eingeschränkte Verständlichkeit von komplexen Stromflächen. Selbstverdeckungen oder Aufrollungen behindern die Form- und Strömungswahrnehmung und machen diese Flächen gerade in interessanten Strömungssituationen wenig nützlich. Auf Basis von handgezeichneten Strömungsdarstellungen haben wir ein Flächenrendering entwickelt, das Silhouetten, nicht-photorealistische Beleuchtung und illustrative Stromlinien verwendet. Interaktive Flächenschnitte erlauben die Exploration der Flächen und der Strömungen, die sie repräsentieren. Angewendet auf verschiedene Stromflächen ließ sich zeigen, dass die Methoden die Verständlichkeit erhöhen, v.a. in Bereichen komplexer Strömung mit Aufwicklungen oder Singularitäten. Das zweite Problem ist die Strömungsanalyse des Blutes aus 4D PC-MRI-Daten. An diese relativ neue Datenmodalität werden hohe Erwartungen für die Erforschung und Behandlung kardiovaskulärer Krankheiten geknüpft, da sie erstmals ein dreidimensionales, zeitlich aufgelöstes Abbild der Hämodynamik liefert. Bisher werden 4D PC-MRI-Daten meist mit Werkzeugen der klassischen Strömungsvisualisierung verarbeitet. Diese werden den besonderen Ansprüchen der medizinischen Anwender jedoch nicht gerecht, die in kurzer Zeit eine übersichtliche Darstellung der relevanten Strömungsaspekte erhalten möchten. Wir haben ein Werkzeug zur visuellen Analyse der Blutströmung entwickelt, welches eine einfache Detektion von markanten Strömungsmustern erlaubt, wie z.B. Jets, Wirbel oder Bereiche mit hoher Blutverweildauer. Die Grundidee ist hierbei aus vorberechneten Integrallinien mit Hilfe speziell definierter Linienprädikate die relevanten, d.h. am gefragten Strömungsmuster, beteiligten Linien ausgewählt werden. Um eine intuitive Darstellung der Resultate zu erreichen, haben wir uns von Blutflußillustrationen inspirieren lassen und präsentieren eine abstrakte Linienbündel- und Wirbeldarstellung. Die Linienprädikatmethode sowie die abstrakte Darstellung der Strömungsmuster wurden an 4D PC-MRI-Daten von gesunden und pathologischen Aorten- und Herzdaten erfolgreich getestet. Auch die Evaluierung durch Experten zeigt die Nützlichkeit der Methode und ihr Potential für den Einsatz in der Forschung und der Klinik.This thesis’ central theme is the use of illustrative methods to solve flow visualization problems. The goal of flow visualization is to provide users with software tools supporting them analyzing and extracting knowledge from their fluid dynamics data. This fluid dynamics data is produced in large amounts by simulations or measurements to answer diverse questions in application fields like engineering or medicine. This thesis deals with two unsolved problems in flow visualization and tackles them with methods of illustrative visualization. The latter is a subbranch of visualization whose methods are inspired by the art work of professional illustrators. They are specialized in the comprehensible and esthetic representation of complex knowledge. With illustrative visualization, their techniques are applied to real data to enhance their representation. The first problem dealt with in this thesis is the limited shape and flow perception of complex stream surfaces. Self-occlusion and wrap-ups hinder their effective use in the most interesting flow situations. On the basis of hand-drawn flow illustrations, a surface rendering method was designed that uses silhouettes, non-photorealistic shading, and illustrative surface stream lines. Additionally, geometrical and flow-based surface cuts allow the user an interactive exploration of the surface and the flow it represents. By applying this illustrative technique to various stream surfaces and collecting expert feedback, we could show that the comprehensibility of the stream surfaces was enhanced – especially in complex areas with surface wrap-ups and singularities.
The second problem tackled in this thesis is the analysis of blood flow from 4D PC-MRI data. From this rather young data modality, medical experts expect many advances in the research of cardiovascular diseases because it delivers a three-dimensional and time-resolved image of the hemodynamics. However, 4D PC-MRI data are mainly processed with standard flow visualizaton tools, which do not fulfill the requirements of medical users. They need a quick and easy-to-understand display of the relevant blood flow aspects. We developed a tool for the visual analysis of blood flow that allows a fast detection of distinctive flow patterns, such as high-velocity jets, vortices, or areas with high residence times. The basic idea is to precalculate integral lines and use specifically designed line predicates to select and display only lines involved in the pattern of interest. Traditional blood flow illustrations inspired us to an abstract and comprehensible depiction of the resulting line bundles and vortices. The line predicate method and the illustrative flow pattern representation were successfully tested with 4D PC-MRI data of healthy and pathological aortae and hearts. Also, the feedback of several medical experts confirmed the usefulness of our methods and their capabilities for a future application in the clinical research and routine
INTERFACE DESIGN FOR A VIRTUAL REALITY-ENHANCED IMAGE-GUIDED SURGERY PLATFORM USING SURGEON-CONTROLLED VIEWING TECHNIQUES
Initiative has been taken to develop a VR-guided cardiac interface that will display and deliver information without affecting the surgeons’ natural workflow while yielding better accuracy and task completion time than the existing setup. This paper discusses the design process, the development of comparable user interface prototypes as well as an evaluation methodology that can measure user performance and workload for each of the suggested display concepts.
User-based studies and expert recommendations are used in conjunction to es tablish design guidelines for our VR-guided surgical platform. As a result, a better understanding of autonomous view control, depth display, and use of virtual context, is attained. In addition, three proposed interfaces have been developed to allow a surgeon to control the view of the virtual environment intra-operatively. Comparative evaluation of the three implemented interface prototypes in a simulated surgical task scenario, revealed performance advantages for stereoscopic and monoscopic biplanar display conditions, as well as the differences between three types of control modalities. One particular interface prototype demonstrated significant improvement in task performance. Design recommendations are made for this interface as well as the others as we prepare for prospective development iterations
Diagnostic Imaging and Radiation Therapy in the Arab World: A New Model of Advanced Practice
This study aimed at suggesting a new model for advanced practice in the diagnostic imaging and radiation therapy in the Arab World by presenting a comparative study between the different medical imaging techniques, the concepts, benefits, risks and medical applications of these techniques has been presented with details. Attempting For building a new model of advanced practice for the diagnostic role of imaging and radiation therapy in the Arab World; by analyzing the current status of the imaging and radiation therapy in the Arab World, and then surveying the different medical imaging techniques. Then to suggest a model of best practices upon the outcomes of the study
A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields
Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context,
the present paper proposes an unsupervised and graph-based method of image segmentation, which is
driven by an application goal, namely, the generation of image segments associated with a user-defined
and application-specific goal. A graph, together with a random grid of source elements, is defined on
top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation
algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring
the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical
modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized
through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven
segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not
require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven
predicate and a suited parametric model for the data. In the experimental validation with both magnetic
resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated
product
Visualization for Biological Models, Simulation, and Ontologies
In this dissertation, I present three browsers that I have developed for the purpose
of exploring, understanding, and analyzing models, simulations, and ontologies in
biology and medicine. The first browser visualizes multidimensional simulation data
as an animation. The second browser visualizes the equations of a complex model as
a network and puts structure and organization on top of equations and variables. The
third browser is an ontology viewer and editor, directly intended for the Foundational
Model of Anatomy (FMA), but applicable to other ontologies as well. This browser
has two contributions. First, it is a lightweight deliverable that lets someone easily
dabble with the FMA. Second, it lets the user edit an ontology to create a view of
it. For the ontology browser, I also conduct user studies to refine and evaluate the
software
A Review on the Role of Nano-Communication in Future Healthcare Systems: A Big Data Analytics Perspective
This paper presents a first-time review of the open literature focused on the significance of big data generated within nano-sensors and nano-communication networks intended for future healthcare and biomedical applications. It is aimed towards the development of modern smart healthcare systems enabled with P4, i.e. predictive, preventive, personalized and participatory capabilities to perform diagnostics, monitoring, and treatment. The analytical capabilities that can be produced from the substantial amount of data gathered in such networks will aid in exploiting the practical intelligence and learning capabilities that could be further integrated with conventional medical and health data leading to more efficient decision making. We have also proposed a big data analytics framework for gathering intelligence, form the healthcare big data, required by futuristic smart healthcare to address relevant problems and exploit possible opportunities in future applications. Finally, the open challenges, future directions for researchers in the evolving healthcare domain, are presented
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