@phdthesis{Schmithausen2019,
  author    = {Schmithausen, Patrick Alexander Gerhard},
  title     = {Three-dimensional fluorescence image analysis of megakaryocytes and vascular structures in intact bone},
  doi       = {10.25972/OPUS-17854},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-178541},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {The thesis provides insights in reconstruction and analysis pipelines for processing of three-dimensional cell and vessel images of megakaryopoiesis in intact murine bone. The images were captured in a Light Sheet Fluorescence Microscope. The work presented here is part of Collaborative Research Centre (CRC) 688 (project B07) of the University of W{\"u}rzburg, performed at the Rudolf-Virchow Center. Despite ongoing research within the field of megakaryopoiesis, its spatio-temporal pattern of megakaryopoiesis is largely unknown. Deeper insight to this field is highly desirable to promote development of new therapeutic strategies for conditions related to thrombocytopathy as well as thrombocytopenia. The current concept of megakaryopoiesis is largely based on data from cryosectioning or in vitro studies indicating the existence of spatial niches within the bone marrow where specific stages of megakaryopoiesis take place. Since classic imaging of bone sections is typically limited to selective two-dimensional views and prone to cutting artefacts, imaging of intact murine bone is highly desired. However, this has its own challenges to meet, particularly in image reconstruction. Here, I worked on processing pipelines to account for irregular specimen staining or attenuation as well as the extreme heterogeneity of megakaryocyte morphology. Specific challenges for imaging and image reconstruction are tackled and solution strategies as well as remaining limitations are presented and discussed. Fortunately, modern image processing and segmentation strongly benefits from continuous advances in hardware as well as software-development. This thesis exemplifies how a combined effort in biomedicine, computer vision, data processing and image technology leads to deeper understanding of megakaryopoiesis. Tailored imaging pipelines significantly helped elucidating that the large megakaryocytes are broadly distributed throughout the bone marrow facing a surprisingly dense vessel network. No evidence was found for spatial niches in the bone marrow, eventually resulting in a revised model of megakaryopoiesis.},
  subject      = {Megakaryozytopoese},
  language  = {en}
}
@phdthesis{Pfitzner2019,
  author    = {Pfitzner, Christian},
  title     = {Visual Human Body Weight Estimation with Focus on Clinical Applications},
  isbn      = {978-3-945459-27-0 (online)},
  doi       = {10.25972/OPUS-17484},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-174842},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {It is the aim of this thesis to present a visual body weight estimation, which is suitable for medical applications. A typical scenario where the estimation of the body weight is essential, is the emergency treatment of stroke patients: In case of an ischemic stroke, the patient has to receive a body weight adapted drug, to solve a blood clot in a vessel. The accuracy of the estimated weight influences the outcome of the therapy directly. However, the treatment has to start as early as possible after the arrival at a trauma room, to provide sufficient treatment. Weighing a patient takes time, and the patient has to be moved. Furthermore, patients are often not able to communicate a value for their body weight due to their stroke symptoms. Therefore, it is state of the art that physicians guess the body weight. A patient receiving a too low dose has an increased risk that the blood clot does not dissolve and brain tissue is permanently damaged. Today, about one-third gets an insufficient dosage. In contrast to that, an overdose can cause bleedings and further complications. Physicians are aware of this issue, but a reliable alternative is missing. The thesis presents state-of-the-art principles and devices for the measurement and estimation of body weight in the context of medical applications. While scales are common and available at a hospital, the process of weighing takes too long and can hardly be integrated into the process of stroke treatment. Sensor systems and algorithms are presented in the section for related work and provide an overview of different approaches. The here presented system -- called Libra3D -- consists of a computer installed in a real trauma room, as well as visual sensors integrated into the ceiling. For the estimation of the body weight, the patient is on a stretcher which is placed in the field of view of the sensors. The three sensors -- two RGB-D and a thermal camera -- are calibrated intrinsically and extrinsically. Also, algorithms for sensor fusion are presented to align the data from all sensors which is the base for a reliable segmentation of the patient. A combination of state-of-the-art image and point cloud algorithms is used to localize the patient on the stretcher. The challenges in the scenario with the patient on the bed is the dynamic environment, including other people or medical devices in the field of view. After the successful segmentation, a set of hand-crafted features is extracted from the patient's point cloud. These features rely on geometric and statistical values and provide a robust input to a subsequent machine learning approach. The final estimation is done with a previously trained artificial neural network. The experiment section offers different configurations of the previously extracted feature vector. Additionally, the here presented approach is compared to state-of-the-art methods; the patient's own assessment, the physician's guess, and an anthropometric estimation. Besides the patient's own estimation, Libra3D outperforms all state-of-the-art estimation methods: 95 percent of all patients are estimated with a relative error of less than 10 percent to ground truth body weight. It takes only a minimal amount of time for the measurement, and the approach can easily be integrated into the treatment of stroke patients, while physicians are not hindered. Furthermore, the section for experiments demonstrates two additional applications: The extracted features can also be used to estimate the body weight of people standing, or even walking in front of a 3D camera. Also, it is possible to determine or classify the BMI of a subject on a stretcher. A potential application for this approach is the reduction of the radiation dose of patients being exposed to X-rays during a CT examination. During the time of this thesis, several data sets were recorded. These data sets contain the ground truth body weight, as well as the data from the sensors. They are available for the collaboration in the field of body weight estimation for medical applications.},
  subject      = {Punktwolke},
  language  = {en}
}