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- Läthén, Gunnar, 1981-
(författare)
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Level Set Segmentation and Volume Visualization of Vascular Trees
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Medical imaging is an important part of the clinical workflow. With the increasing amount and complexity of image data comes the need for automatic (or semi-automatic) analysis methods which aid the physician in the exploration of the data. One specific imaging technique is angiography, in which the blood vessels are imaged using an injected contrast agent which increases the contrast between blood and surrounding tissue. In these images, the blood vessels can be viewed as tubular structures with varying diameters. Deviations from this structure are signs of disease, such as stenoses introducing reduced blood flow, or aneurysms with a risk of rupture. This thesis focuses on segmentation and visualization of blood vessels, consituting the vascular tree, in angiography images.Segmentation is the problem of partitioning an image into separate regions. There is no general segmentation method which achieves good results for all possible applications. Instead, algorithms use prior knowledge and data models adapted to the problem at hand for good performance. We study blood vessel segmentation based on a two-step approach. First, we model the vessels as a collection of linear structures which are detected using multi-scale filtering techniques. Second, we develop machine-learning based level set segmentation methods to separate the vessels from the background, based on the output of the filtering.In many applications the three-dimensional structure of the vascular tree has to be presented to a radiologist or a member of the medical staff. For this, a visualization technique such as direct volume rendering is often used. In the case of computed tomography angiography one has to take into account that the image depends on both the geometrical structure of the vascular tree and the varying concentration of the injected contrast agent. The visualization should have an easy to understand interpretation for the user, to make diagnostical interpretations reliable. The mapping from the image data to the visualization should therefore closely follow routines that are commonly used by the radiologist. We developed an automatic method which adapts the visualization locally to the contrast agent, revealing a larger portion of the vascular tree while minimizing the manual intervention required from the radiologist. The effectiveness of this method is evaluated in a user study involving radiologists as domain experts.
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| 2. |
- Sundvall, Erik, 1973-
(författare)
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Scalability and Semantic Sustainability in Electronic Health Record Systems
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This work is a small contribution to the greater goal of making software systems used in healthcare more useful and sustainable. To come closer to that goal, health record data will need to be more computable and easier to exchange between systems.Interoperability refers to getting systems to work together and semantics concerns the study of meanings. If Semantic interoperability is achieved then information entered in one information system is usable in other systems and reusable for many purposes. Scalability refers to the extent to which a system can gracefully grow by adding more resources. Sustainability refers more to how to best use available limited resources. Both aspects are important.The main focus and aim of the thesis is to increase knowledge about how to support scalability and semantic sustainability. It reports explorations of how to apply aspects of the above to Electronic Health Record (EHR) systems, associated infrastructure, data structures, terminology systems, user interfaces and their mutual boundaries.Using terminology systems is one way to improve computability and comparability of data. Modern complex ontologies and terminology systems can contain hundreds of thousands of concepts that can have many kinds of relationships to multiple other concepts. This makes visualization challenging. Many visualization approaches designed to show the local neighbourhood of a single concept node do not scale well to larger sets of nodes. The interactive TermViz approach described in this thesis, is designed to aid users to navigate and comprehend the context of several nodes simultaneously. Two applications are presented where TermViz aids management of the boundary between EHR data structures and the terminology system SNOMED CT.The amount of available time from people skilled in health informatics is limited. Adequate methods and tools are required to develop, maintain and reuse health-IT solutions in a sustainable way. Multiple levels of modelling including a fixed reference model and another layer of flexible reusable ‘archetypes’ for domain specific data structures, is an approach with that aim used in openEHR and the ISO 13606 standard. This approach, including learning, implementing and managing it, is explored from different angles in this thesis. An architecture applying Representational State Transfer (REST) to archetype-based EHR systems, in order to address scalability, is presented. Combined with archetyping this architecture also aims at enabling a sustainable way of continuously evolving multi-vendor EHR solutions. An experimental open source implementation of it, aimed for learning and prototyping, is also presented.Manually changing database structures used for storage every time new versions of archetypes and associated data structures are needed is likely not a sustainable activity. Thus storage systems that can handle change with minimal manual interventions are desirable. Initial explorations of performance and scalability in such systems are also reportedGraphical user interfaces focused on EHR navigation, time-perspectives and highlighting of EHR content are also presented – illustrating what can be done with computable health record data and the presented approaches.Desirable aspects of semantic sustainability have been discussed, including: sustainable use of limited resources (such as available time of skilled people), and reduction of unnecessary risks. A semantic sustainability perspective should be inspired and informed by research in complex systems theory, and should also include striving to be highly aware of when and where technical debt is being built up. Semantic sustainability is a shared responsibility.The combined results presented contribute to increasing knowledge about ways to support scalability and semantic sustainability in the context of electronic health record systems. Supporting tools, architectures and approaches are additional contributions.
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