Publications by authors named "Ivo Baltruschat"
Article Synopsis
- Magnesium (Mg) alloys, particularly those with gadolinium, are emerging as promising materials for temporary bone implants due to their biocompatibility and mechanical properties, presenting a potential replacement for traditional titanium and stainless-steel implants.
- A study involving rat tibias over various time periods (10, 20, and 32 weeks) used advanced imaging techniques to evaluate the implants' degradation behavior and their integration with bone tissues.
- Results indicate that the Mg-xGd implants not only form a stable degradation layer and support bone remodeling similar to titanium but also do not accumulate harmful levels of Mg or Gd in organs, making them suitable for use in bone repair.
Highly accurate segmentation of large 3D volumes is a demanding task. Challenging applications like the segmentation of synchrotron radiation microtomograms (SRμCT) at high-resolution, which suffer from low contrast, high spatial variability and measurement artifacts, readily exceed the capacities of conventional segmentation methods, including the manual segmentation by human experts. The quantitative characterization of the osseointegration and spatio-temporal biodegradation process of bone implants requires reliable, and very precise segmentation.
View Article and Find Full Text PDFObjective: The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI-resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist.
Methods: We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution.
Objective: This paper addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectures for image classification. The second problem is the high-class imbalance encountered in real-world multi-class datasets.
View Article and Find Full Text PDFThe increased availability of labeled X-ray image archives (e.g. ChestX-ray14 dataset) has triggered a growing interest in deep learning techniques.
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