Medizinische Informatik
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Institute
Segmentation of the Cerebrospinal Fluid from MRI Images for the Treatment of Disc Herniations
(2010)
About 80 percent of people are affected at some point in their lives by lower back pain, which is one of the most common neurological diseases and reasons for long-term disability in the United States. The symptoms are primarily caused by overly heavy lifting and/or overstretching of the back, leading to a rupture and an outward bulge of an intervertebral disc, which puts pressure on and pinches the nerve fibers of the spine. The most common form is a lumbar disc herniation between the fourth and fifth lumbar vertebra and between the fifth lumbar vertebra and the sacrum. In recent years the diagnosis of lower back pain has improved, mainly due to enhanced imaging techniques and imaging quality, but the surgical therapy remains hazardous. Reasons for this include low visibility when accessing the lumbar area and the high risk of causing permanent damage when touching the nerve fibers. A new approach for increasing patient safety is the segmentation and visualization of the cerebrospinal fluid in the lower lumbar region of the vertebral column. For this purpose a new fully-automatic and a semi-automatic approach were developed for separating the cerebrospinal fluid from its surroundings on T2-weighted MRI scans of the lumbar vertebra. While the fully-automatic algorithm is realized by a model-based searching method and a volume-based segmentation, the semi-automatic algorithm requires a seed point and performs the segmentation on individual axial planes through a combination of a region-based segmentation algorithm and a thresholding filter. Both algorithms have been applied to four T2-weighted MRI datasets and are compared with a gold-standard segmentation. The segmentation overlap with the gold-standard was 78.7 percent for the fully-automatic algorithm and 93.1 percent for the semi-automatic algorithm. In the pathological region the fully-automatic algorithm obtained a similarity of 56.6 percent, compared to 87.8 percent for the semi-automatic algorithm.
This thesis presents a photmetric stereo method based on the work of Schulze [35], who in turn extended the research of Schroeder et al. [33,34] In this approach, three different lightings are obtained by illuminating the object by three colored light sources (red, green and blue). A video of the subject is captured from the front, the back and the side. The single frames are then extracted from the viedo, which are used for the 3D reconstruction of the subject. The aim of this work was to improve the presented method of Schulze with real patient subjects by getting a better sphere calibration and changing some parameters in the patient processing. As the graphical interface was implemented for persons with a technical background, it has been changed to become also more convenient to use for non-technically oriented staff
Access, Handling and Visualization Tools for Multiple Data Types for Breast Cancer Decision Support
(2011)
Breast cancer is the most commonly diagnosed cancer among U.S women, besides skin cancer. More than 1 in 4 cancers among women are breast cancer. And though death rates have been decreasing since 1990, about 40,170 women in the U.S. were expected to die in 2009 from breast cancer. The progress of molecular profiling, in the last decade has revolutionized the understanding of cancer, but also introduced more complexity with new data such as gene expression, copy number variation, mutations and DNA methylation. These new data open up the possibility of differential diagnosis, much more precise prognosis as well as prediction of therapy response than any of the diagnostic tools that are available in the current practice. Additionally, epidemiological databases store clinically relevant information on hundreds of thousands of patients. However, with the abundance of all this information, clinicians will need new tools to access and visualize such data and use the information gained to treat new patients. The general problem will be to access, filter and analyze the data and then visualize them in a clinical context. This data ranges from clinico-pathological information, to molecular profiles from highthroughput genomic measurements and imaging data. Furthermore, data from patient populations is aggregated on epidemiological level and can be found under numerous clinical studies.
Medication reconciliation is defined by the American Society of Health- System Pharmacists (ASHP) and the American Pharmacists Association (AphA) as “the comprehensive evaluation of a patient’s medication regimen any time there is a change in therapy in an effort to avoid medication errors such as omissions, duplications, dosing errors or drug interactions, as well as to observe compliance and adherence patterns “. Medication reconciliation is very important to avoid medication errors but it is also a complex and time-consuming process. Medication histories, i.e. records of prescription, purchase, and refill sequences are considered to be a resource from which conclusions about medication reconciliation can be drawn. However, medication histories spread across diverse paper and electronic media may lack the required accuracy. By employing multiple electronic sources this thesis will evaluate if more accurate medication histories can be collected.