Multimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential. The goal of this study is the evaluation of established segmentation methods on different medical image data sets acquired with different diagnostic procedures. Established segmentation methods were implemented using the latest state of the art and applied to medical image data sets. In order to benchmark the segmentation performance quantitatively, medical image data sets were superimposed with artificial Gaussian noise, and the mis-segmentation as a function of the image SNR or CNR was compared to a gold standard. The evaluation of the image segmentation showed that the best results of pixel-based segmentation ( ) can be achieved with methods of machine learning, multithreshold and advanced level-set method -even at high artificial noise (SNR ). Finally, the complexity of the object geometry and the contrast of the ROI to the surrounding tissue must also be considered to select the best segmentation algorithm.
The risk of healthcare associated infections (HAI) is rising with the utilization of more complex medical devices. Cleaning and disinfecting measures of such devices are often insufficient leading to an increased microbiological contamination on these devices. Recent studies imply that antimicrobial coatings could present a solution for this topic. In this work a novel approach for the introduction of an antimicrobial technology into plastic granulate was tested. After 3-D printing the antimicrobial activity of the test samples was analysed. Our results show that the integration of an antimicrobial substance to ABS plastic is feasible only with sophisticated plastic processing technologies. Simple heating or mixing of the substance did not allow integration of the antimicrobial substance into the 3-D printed sample, but it was possible to integrate the antimicrobial ingredient into the raw material by compounding. The printed test samples showed strong antimicrobial activity in the standardized test procedures.
The risk of infection from contaminated surfaces has already been shown in several publications. Due to the increased demand for optimized infection control measures during the Corona pandemic, antimicrobial surface technologies have gained more an interest. Apart from many proofs of efficacy, there are only a few studies dealing with the durability of these surface coatings with regard to the material and the reprocessing measures. This work did therefore examine the impact of different materials and surface textures, as well as different detergents and disinfectants, on the durability of antimicrobial surface technologies. Differently structured materials (glass, wood, plastics, metal) and wallpaper bonded to plasterboard were coated with an TiO2Ag based antimicrobial coating (HECOSOL GmbH, Bamberg). These test samples are then used to perform abrasion tests with various cleaning and disinfecting agents and cloth systems (microfiber cloth, cotton cloth, foam cloth). The majority of the test samples in our experimental setup showed at least significant activity. According to our results, both the selection of cleaning and disinfection methods including wiping systems and the surface material have a major impact on the durability of antimicrobial coatings. In order to be able to come to conclusions about the long-term activity of these surface technologies, the effectiveness should be tested not only during the development phase, but also in the finished product and again after several reprocessing cycles in use.
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