Workflow automation of SEM acquisitions and feature tracking.

Acquiring multiple high magnification, high resolution images with scanning electron microscopes (SEMs) for quantitative analysis is a time consuming and repetitive task for microscopists. We propose a workflow to automate SEM image acquisition and demonstrate its use in the context of nanoparticle (NP) analysis. Acquiring multiple images of this type of specimen is necessary to obtain a complete and proper characterization of the NP population and obtain statistically representative results.

Advanced characterisation of 3D structure and porosity of ordinary portland cement (OPC) mortar using plasma focused ion beam tomography and X-ray computed tomography.

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.

Scanning Electron Microscopy versus Transmission Electron Microscopy for Material Characterization: A Comparative Study on High-Strength Steels.

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.

Optimization of 3D network topology for bioinspired design of stiff and lightweight bone-like structures.

A truly bioinspired approach to design optimization should follow the energetically favorable natural paradigm of "minimum inventory with maximum diversity". This study was inspired by constructive regression of trabecular bone - a natural process of network connectivity optimization occurring early in skeletal development. During trabecular network optimization, the original excessively connected network undergoes incremental pruning of redundant elements, resulting in a functional and adaptable structure operating at lowest metabolic cost.

Deep learning for 3D imaging and image analysis in biomineralization research.

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.

Altered topological blueprint of trabecular bone associates with skeletal pathology in humans.

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.

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability.

Tablet defects encountered during the manufacturing of oral formulations can result in quality concerns, timeline delays, and elevated financial costs. Internal tablet cracking is not typically measured in routine inspections but can lead to batch failures such as tablet fracturing. X-ray computed tomography (XRCT) has become well-established to analyze internal cracks of oral tablets.

Impact of contrast injection and stent-graft implantation on reproducibility of volume measurements in semiautomated segmentation of abdominal aortic aneurysm on computed tomography.

Purpose: To assess the impact of contrast injection and stent-graft implantation on feasibility, accuracy, and reproducibility of abdominal aortic aneurysm (AAA) volume and maximal diameter (D-max) measurements using segmentation software.

Materials And Methods: CT images of 80 subjects presenting AAA were divided into four equal groups: with or without contrast enhancement, and with or without stent-graft implantation. Semiautomated software was used to segment the aortic wall, once by an expert and twice by three readers.

Clinical validation of semi-automated software for volumetric and dynamic contrast enhancement analysis of soft tissue venous malformations on magnetic resonance imaging examination.

Objectives: To evaluate venous malformation (VM) volume and contrast-enhancement analysis on magnetic resonance imaging (MRI) compared with diameter evaluation.

Methods: Baseline MRI was undertaken in 44 patients, 20 of whom were followed by MRI after sclerotherapy. All patients underwent short-tau inversion recovery (STIR) acquisitions and dynamic contrast assessment.

Seeded ND medical image segmentation by cellular automaton on GPU.

Purpose: We present a GPU-based framework to perform organ segmentation in N-dimensional (ND) medical image datasets by computation of weighted distances using the Ford-Bellman algorithm (FBA). Our GPU implementation of FBA gives an alternative and optimized solution to other graph-based segmentation techniques.

Methods: Given a number of K labelled-seeds, the segmentation algorithm evolves and segments the ND image in K objects.