Inverted Hartmann mask made by deep X-ray lithography for single-shot multi-contrast X-ray imaging with laboratory setup.

This paper reports on the fabrication and characterization of an inverted Hartmann mask and its application for multi-contrast X-ray imaging of polymer composite material in a laboratory setup. Hartmann masks open new possibilities for high-speed X-ray imaging, obtaining orientation-independent information on internal structures without rotating the object. The mask was manufactured with deep X-ray lithography and gold electroplating on a low-absorbing polyimide substrate. Such an approach allows us to produce gratings with a small period and high aspect ratio, leading to a higher spatial resolution and extension towards higher X-ray energies. Tuning the manufacturing process, we achieved a homogeneous patterned area without supporting structures, thus avoiding losses on visibility. We tested mask performance in a laboratory setup with a conventional flat panel detector and assessed mask imaging capabilities using a tailored phantom sample of various sizes. We performed multi-modal X-ray imaging of epoxy matrix polymer composites reinforced with glass fibers and containing microcapsules filled with a healing agent. Hartmann masks made by X-ray lithography enabled fast-tracking of structural changes in low absorbing composite materials and of a self-healing mechanism triggered by mechanical stress.

Introducing Biomedisa as an open-source online platform for biomedical image segmentation.

We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.