Michael Götz

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We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented(More)
BACKGROUND AND PURPOSE The anterior communicating artery (ACoA) is a site of predilection for intracranial saccular aneurysms causing subarachnoid hemorrhage. ACoA aneurysms are frequently associated with an asymmetrical circle of Willis. In such cases, the ACoA is probably exposed to high hemodynamic stress caused by a considerable shunt flow across the(More)
PURPOSE Antiangiogenic treatment with bevacizumab, a mAb to the VEGF, is the single most widely used therapeutic agent for patients with recurrent glioblastoma. A major challenge is that there are currently no validated biomarkers that can predict treatment outcome. Here we analyze the potential of radiomics, an emerging field of research that aims to(More)
Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the(More)
Current developments in the health care sector are marked by the increased digitalisation of patient records, the use of electronic devices as supporting tools in patient care, and the employment of sensors (e.g. monitoring devices and surgery recording devices), which contribute directly to the abundance of medical data. However, before any significant(More)
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached(More)
Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing(More)
BACKGROUND AND PURPOSE ABC/2 is still widely accepted for volume estimations in spontaneous intracerebral hemorrhage (ICH) despite known limitations, which potentially accounts for controversial outcome-study results. The aim of this study was to establish and validate an automatic segmentation algorithm, allowing for quick and accurate quantification of(More)