Reconstruction of highly porous structures from FIB-SEM using a deep neural network trained on synthetic images.

Combining scanning electron microscopy with serial slicing by a focused ion beam yields spatial image data of materials structures at the nanometer scale. However, the depth of field of the scanning electron microscopic images causes unwanted effects when highly porous structures are imaged. Proper spatial reconstruction of such porous structures from the stack of microscopic images is a tough and in general yet unsolved segmentation problem. Recently, machine learning methods have proven to yield solutions to a variety of image segmentation problems. However, their use is hindered by the need of large amounts of annotated data in the training phase. Here, we therefore replace annotated real image data by simulated image stacks of synthetic structures - realizations of stochastic germ-grain models and random packings. This strategy yields the annotations for free, but shifts the effort to choosing appropriate stochastic geometry models and generating sufficiently realistic scanning electron microscopic images.

A stochastic microstructure model for particle reinforced aluminium matrix composites.

Metal matrix composites are complex materials consisting of various phases which can display largely different mechanical properties. The deformation behaviour of these composites cannot be sufficiently modelled by averages or simple particle shapes due to the local stresses that occur on the particle edges. Therefore, a sophisticated model of the microstructure is needed. We introduce a method for stochastic modelling of a silicon carbide (SiC) particle reinforced aluminium matrix composite. The SiC particles are modelled by Laguerre polyhedra generated by densely packed spheres. The shape factors of the polyhedra have been fitted to the particle shapes observed in three-dimensional images. Particle elongation in extrusion direction and the observed log-normal volume distribution of the particles are included in the model by suitable scaling. An outlook is presented on how to model the grains of the polycrystalline aluminium matrix and intermetallic precipitates, which result from the strengthening mechanism of the matrix. LAY DESCRIPTION: Metal matrix composites are complex materials consisting of different phases which can display largely different mechanical properties. The deformation behaviour of these composites cannot be sufficiently modelled by averages or simple particle shapes due to the local stresses that occur on the particle edges. Therefore, a sophisticated model of the microstructure is needed. We introduce a method of stochastic modelling of a silicon carbide (SiC) particle reinforced aluminium matrix composite. The SiC particles are modelled by Laguerre polyhedra generated by densely packed spheres. The shape factors of the polyhedra have been fitted to the SiC shapes observed in three-dimensional images. Additionally, the polyhedra are scaled anisotropically to account for orientation anisotropy and to obtain a log-normal volume distribution. An outlook is presented on how to model the aluminium phase's grains and intermetallic precipitates, which result from the strengthening mechanism of the aluminium matrix alloy.

Three-dimensional image analytical detection of intussusceptive pillars in murine lung.

A variety of diseases can lead to loss of lung tissue. Currently, this can be treated only symptomatically. In mice, a complete compensatory lung growth within 21 days after resection of the left lung can be observed. Understanding and transferring this concept of compensatory lung growth to humans would greatly improve therapeutic options. Lung growth is always accompanied by a process called angiogenesis forming new capillary blood vessels from preexisting ones. Among the processes during lung growth, the formation of transluminal tissue pillars within the capillary vessels (intussusceptive pillars) is observed. Therefore, pillars can be understood as an indicator for active angiogenesis and microvascular remodelling. Thus, their detection is very valuable when aiming at characterization of compensatory lung growth. In a vascular corrosion cast, these pillars appear as small holes that pierce the vessels. So far, pillars were detected visually only based on 2D images. Our approach relies on high-resolution synchrotron microcomputed tomographic images. With a voxel size of 370 nm we exploit the spatial information provided by this imaging technique and present the first algorithm to semiautomatically detect intussusceptive pillars. An at least semiautomatic detection is essential in lung research, as manual pillar detection is not feasible due to the complexity and size of the 3D structure. Using our algorithm, several thousands of pillars can be detected and subsequently analysed, e.g. regarding their spatial arrangement, size and shape with an acceptable amount of human interaction. In this paper, we apply our novel pillar detection algorithm to compute pillar densities of different specimens. These are prepared such that they show different growing states. Comparing the corresponding pillar densities allows to investigate lung growth over time.