Titlebook: Bildverarbeitung für die Medizin 2020; Algorithmen – System Thomas Tolxdorff,Thomas M. Deserno,Christoph Palm Conference proceedings 2020 S

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https://doi.org/10.1007/978-3-658-39143-0gmentierungsbarrieren als Nebenbedingungen helfen hierbei. Aktuell werden diese Markierungen häufig noch als manuelle Scribbles vom Benutzer i.d.R. mühsam schichtweise erstellt. Hier wird ein neuer vollautomatischer Ansatz zum Finden von virtuellen 3D-Barrieren mit maschinellen Lernmethoden vorgeste

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https://doi.org/10.1007/978-3-658-39143-0al diagnostics in hematology [1]. Having been practised for well over a century, cytomorphological analysis is still today routinely performed by human examiners using optical microscopes, a process that can be tedious, time-consuming, and suffering from considerable intra-and inter-rater variabilit

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https://doi.org/10.1007/978-3-658-39143-0after various surgical procedures and in different medical treatment methods. At the same time, the major psoas muscle has been has been used as a tool to assess total muscle volume. From the image processing side it has the advantage of being one of the few muscles that are not surrounded by other

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Post-Islamism Versus Neoconservatism,nd Parkinson’s Disease (PD). The majority of studies to date have relied on using manual segmentation methods to segment the LC, which is time consuming and leads to substantial interindividual variability across raters. Automated segmentation approaches might be less error-prone leading to a higher

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https://doi.org/10.1007/978-1-349-61373-1ypes represent an important risk factor of metabolic disorders. Currently, the gold standard for measuring volumes of VAT and SAT is the manual segmentation of abdominal fat images from 3D Dixon magnetic resonance (MR) scans – a very expensive and time-consuming process. To this end, we recently pro

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Islamism and Secularism in North Africausually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-

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https://doi.org/10.1007/978-1-349-61373-1odelling of dynamic contrast-enhanced (DCE) MRI data and apparent diffusion coefficient calculations. There are many fitting tools available, however most of them are limited to a special purpose and do not allow for own development and extension.

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https://doi.org/10.1007/978-1-349-61373-1 tomography (OCT) scans of the retina allow the computation of motion contrast to display the retinal vasculature. To the best of our knowledge, we present the first application of compressed sensing for the generation of OCTA volumes. Using a probabilistic signal model for the computation of OCTA v