Focused ion beam (FIB) tomography is a destructive technique used to collect three-dimensional (3D) structural information at a resolution of a few nanometers. For FIB tomography, a material sample ...is degraded by layer-wise milling. After each layer, the current surface is imaged by a scanning electron microscope (SEM), providing a consecutive series of cross-sections of the three-dimensional material sample. Especially for nanoporous materials, the reconstruction of the 3D microstructure of the material, from the information collected during FIB tomography, is impaired by the so-called
shine-through effect
. This effect prevents a unique mapping between voxel intensity values and material phase (e.g., solid or void). It often substantially reduces the accuracy of conventional methods for image segmentation. Here we demonstrate how machine learning can be used to tackle this problem. A bottleneck in doing so is the availability of sufficient training data. To overcome this problem, we present a novel approach to generate synthetic training data in the form of FIB-SEM images generated by Monte Carlo simulations. Based on this approach, we compare the performance of different machine learning architectures for segmenting FIB tomography data of nanoporous materials. We demonstrate that two-dimensional (2D) convolutional neural network (CNN) architectures processing a group of adjacent slices as input data as well as 3D CNN perform best and can enhance the segmentation performance significantly.
Bone is a hierarchically organized biological material, and its strength is usually attributed to overt factors such as mass, density, and composition. Here we investigate a covert factor – the ...topological blueprint, or the network organization pattern of trabecular bone. This generally conserved metric of an edge-and-node simplified presentation of trabecular bone relates to the average coordination/valence of nodes and the equiangular 3D offset of trabeculae emanating from these nodes. We compare the topological blueprint of trabecular bone in presumably normal, fractured osteoporotic, and osteoarthritic samples (all from human femoral head, cross-sectional study). We show that bone topology is altered similarly in both fragility fracture and in joint degeneration. Decoupled from the morphological descriptors, the topological blueprint subjected to simulated loading associates with an abnormal distribution of strain, local stress concentrations and lower resistance to the standardized load in pathological samples, in comparison with normal samples. These topological effects show no correlation with classic morphological descriptors of trabecular bone. The negative effect of the altered topological blueprint may, or may not, be partly compensated for by the morphological parameters. Thus, naturally occurring optimization of trabecular topology, or a lack thereof in skeletal disease, might be an additional, previously unaccounted for, contributor to the biomechanical performance of bone, and might be considered as a factor in the life-long pathophysiological trajectory of common bone ailments.
•Mechanical performance of the skeleton results from many factors and their interplay.•Topological blueprint as a basic trabecular design plan is an understudied factor.•Topological blueprint deviation undermines mechanical properties of trabecular bone.•Higher bone mass or thicker trabeculae do not compensate for deviant topology.
A deep learning procedure has been examined for automatic segmentation of 3D tomography images from fiber-reinforced ceramic composites consisting of fibers and matrix of the same material (SiC), and ...thus identical image intensities. The analysis uses a neural network to distinguish phases from shape and edge information rather than intensity differences. It was used successfully to segment phases in a unidirectional composite that also had a coating with similar image intensity. It was also used to segment matrix cracks generated during in situ tensile loading of the composite and thereby demonstrate the influence of nonuniform fiber distribution on the nature of matrix cracking. By avoiding the need for manual segmentation of thousands of image slices, the procedure overcomes a major impediment to the extraction of quantitative information from such images. The analysis was performed using recently developed software that provides a general framework for executing both training and inference.
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The visualisation and quantification of pore networks and main phases have been critical research topics in cementitious materials as many critical mechanical and chemical properties and ...infrastructure reliability rely on these 3D characteristics. In this study, we realised the mesoscale serial sectioning and analysis up to ∼80 μm by ∼90 μm by ∼60 μm on portland cement mortar using plasma focused ion beam (PFIB) for the first time. The workflow of working with mortar and PFIB was established applying a prepositioned hard silicon mask to reduce curtaining. Segmentation with minimal human interference was performed using a trained neural network, in which multiple types of segmentation models were compared. Combining PFIB analysis at microscale with X‐ray micro‐computed tomography, the analysis of capillary pores and air voids ranging from hundreds of nanometres (nm) to millimetres (mm) can be conducted. The volume fraction of large capillary pores and air voids are 11.5% and 12.7%, respectively. Moreover, the skeletonisation of connected capillary pores clearly shows fluid transport pathways, which is a key factor determining durability performance of concrete in aggressive environments. Another interesting aspect of the FIB tomography is the reconstruction of anhydrous phases, which could enable direct study of hydration kinetics of individual cement phases.
The microstructures of quenched and tempered steels have been traditionally explored by transmission electron microscopy (TEM) rather than scanning electron microscopy (SEM) since TEM offers the high ...resolution necessary to image the structural details that control the mechanical properties. However, scanning electron microscopes, apart from providing larger area coverage, are commonly available and cheaper to purchase and operate compared to TEM and have evolved considerably in terms of resolution. This work presents detailed comparison of the microstructure characterization of quenched and tempered high-strength steels with TEM and SEM electron channeling contrast techniques. For both techniques, similar conclusions were made in terms of large-scale distribution of martensite lath and plates and nanoscale observation of nanotwins and dislocation structures. These observations were completed with electron backscatter diffraction to assess the martensite size distribution and the retained austenite area fraction. Precipitation was characterized using secondary imaging in the SEM, and a deep learning method was used for image segmentation. In this way, carbide size, shape, and distribution were quantitatively measured down to a few nanometers and compared well with the TEM-based measurements. These encouraging results are intended to help the material science community develop characterization techniques at lower cost and higher statistical significance.
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Biomineralization research examines structure-function relations in all types of exo- and endo-skeletons and other hard tissues of living organisms, and it relies heavily on 3D ...imaging. Segmentation of 3D renderings of biomineralized structures has long been a bottleneck because of human limitations such as our available time, attention span, eye-hand coordination, cognitive biases, and attainable precision, amongst other limitations. Since recently, some of these routine limitations appear to be surmountable thanks to the development of deep-learning algorithms for biological imagery in general, and for 3D image segmentation in particular. Many components of deep learning often appear too abstract for a life scientist. Despite this, the basic principles underlying deep learning have many easy-to-grasp commonalities with human learning and universal logic. This primer presents these basic principles in what we feel is an intuitive manner, without relying on prerequisite knowledge of informatics and computer science, and with the aim of improving the reader’s general literacy in artificial intelligence and deep learning. Here, biomineralization case studies are presented to illustrate the application of deep learning for solving segmentation and analysis problems of 3D images ridden by various artifacts, and/or which are plainly difficult to interpret. The presented portfolio of case studies includes three examples of imaging using micro-computed tomography (µCT), and three examples using focused-ion beam scanning electron microscopy (FIB-SEM), all on mineralized tissues. We believe this primer will expand the circle of users of deep learning amongst biomineralization researchers and other life scientists involved with 3D imaging, and will encourage incorporation of this powerful tool into their professional skillsets and to explore it further.
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