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In experiments on the fruit fly model organism Drosophila melanogaster, Heidelberg University biologists gained new insight into how feeding behaviour is…
The invention offers a plasma protein biomarker set that can be used as a diagnostic tool for diagnosis and stratification of colorectal cancer.
The study with participation of the Helmholtz Centre for Environmental Research (UFZ), the German Centre for Integrative Biodiversity Research (iDiv) and the…
After the Big Bang 13.8 billion years ago, the universe cooled down rapidly, and within less than a million years the cosmos became completely dark. The…
A modern fuel cell is usually made of a stack of individual cells that are mechanically pressed together. In MOD-FC the pressing takes place not mechanically but hydraulically. Due to the complete flushing of separate individual cells with the hydraulic medium, a homogeneous pressing of the inner cell components is ensured. In addition, the hydraulic medium can be directly used as a cooling medium. This ensures a homogeneous electric current generation among the surface and also that no life shortening hot spots appear.
By the nearly ideal operating conditions MOD-FC are capable to examine in-situ membrane electrode units. First functional models are already in use (see figure). A plurality of simultaneously reproducible samples can be examined under the same operating conditions, which improve the quality of test results and reduces significantly the amount of sampling tests. Moreover, individual elements can be replaced by separate individual cells without great effort, due to the modular design of the stacks. In contrast to conventional fuel cell stacks MOD-FC can thus be maintained at a cellular basis.
The inventions are serving to segment organs or organ areas out of multi-channeled volumetric MRT data sets.
To do so, the first invention uses segmentation outcomes from Trial data, which was generated by hand and recorded by several people. After recording an MRT dataset, a first pre-segmentation method is used.In this case, the grayscale information from the MRT data is used along with characteristic regions to create probability data for the organ.
A further development of this technique delivers the possibility to use the image slices from the training data to determine at least three classes of characteristic shape. For every class a support vector machine is trained. By using an exclusion method on each other they are able to detect the characteristic organ form. Based on this segmented area a patient specific probability data set is generated, on which a further segmentation technique is used. By combining both methods, a reliable fully automated segmentation is possible, with one or more emphases. Furthermore this application is less susceptible to variability in the MRT data. It is also possible to process one channel volumetric data.