Image Segmentation for FIB-SEM Serial Sectioning of a Si/C-Graphite Composite Anode Microstructure Based on Preprocessing and Global Thresholding

Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.

Effects of tissue degradation by collagenase and elastase on the biaxial mechanics of porcine airways

BACKGROUND

Common respiratory illnesses, such as emphysema and chronic obstructive pulmonary disease, are characterized by connective tissue damage and remodeling. Two major fibers govern the mechanics of airway tissue: elastin enables stretch and permits airway recoil, while collagen prevents overextension with stiffer properties. Collagenase and elastase degradation treatments are common avenues for contrasting the role of collagen and elastin in healthy and diseased states; while previous lung studies of collagen and elastin have analyzed parenchymal strips in animal and human specimens, none have focused on the airways to date.

METHODS

Specimens were extracted from the proximal and distal airways, namely the trachea, large bronchi, and small bronchi to facilitate evaluations of material heterogeneity, and subjected to biaxial planar loading in the circumferential and axial directions to assess airway anisotropy. Next, samples were subjected to collagenase and elastase enzymatic treatment and tensile tests were repeated. Airway tissue mechanical properties pre- and post-treatment were comprehensively characterized via measures of initial and ultimate moduli, strain transitions, maximum stress, hysteresis, energy loss, and viscoelasticity to gain insights regarding the specialized role of individual connective tissue fibers and network interactions.

RESULTS

Enzymatic treatment demonstrated an increase in airway tissue compliance throughout loading and resulted in at least a 50% decrease in maximum stress overall. Strain transition values led to significant anisotropic manifestation post-treatment, where circumferential tissues transitioned at higher strains compared to axial counterparts. Hysteresis values and energy loss decreased after enzymatic treatment, where hysteresis reduced by almost half of the untreated value. Anisotropic ratios exhibited axially led stiffness at low strains which transitioned to circumferentially led stiffness when subjected to higher strains. Viscoelastic stress relaxation was found to be greater in the circumferential direction for bronchial airway regions compared to axial counterparts.

CONCLUSION

Targeted fiber treatment resulted in mechanical alterations across the loading range and interactions between elastin and collagen connective tissue networks was observed. Providing novel mechanical characterization of elastase and collagenase treated airways aids our understanding of individual and interconnected fiber roles, ultimately helping to establish a foundation for constructing constitutive models to represent various states and progressions of pulmonary disease.

Quantitative FIB/SEM tomogram analysis of closed and open porosity of spheroidized graphite anode materials for LiBs applications

The electrochemical behaviour of rounded graphite particles as anode material in a lithium-ion battery strongly depends on the particle properties. The spheroidization process directly affects these properties, including the open porosity that determines the extent of direct contact between liquid electrolyte and carbon surface. Therefore, the quantification of the proportion between open and closed pores is of great interest. Here, we quantify the open and closed porosity of spheroidized porous graphite particles from FIB-SEM tomograms. Quantification is achieved based on two developments: (1) a new sample preparation strategy and (2) a newly developed image evaluation scheme based on neural networks. The sample preparation strategy involves embedding of many graphite powder particles in indium enabling the investigation of several graphite particles in one FIB/SEM tomogram with high stability and with high contrast between the conductive embedding material and porous graphite. A quantitative evaluation of closed and open porosity is achieved by machine learning in form of convolutional neural networks. The convolutional neural network is used to detect the bulk graphite and by further morphological operations, closed and open pores are identified. An error is determined by comparing automatically created quantifications with manual reference values. Our porosity data for two differently spheroidized graphite samples agree qualitatively well with corresponding results from nitrogen physisorption measurements. This approach may allow quantitative data evaluation from porous powders and support understanding of the correlation to the electrochemical behaviour in the lithium-ion battery.

Investigation of the Effect of 3D Meniscus Geometry on Fluid Dynamics and Crystallization via In Situ Optical Microscopy-Assisted Mathematical Modeling

Solution-based thin-film solidification is a complex process involving various transport phenomena that are intricately dependent on multiple experimental parameters. The difficulty of analyzing this process experimentally or conducting exact numerical simulation make it challenging to understand, predict, and control the solidification process. In this work, a simple and effective technique to analyze the thin-film solidification process during solution shearing, based on 3D geometrical model of the meniscus, is proposed. The 3D meniscus geometry, which changes depending on the experimental parameters, is attained using high-speed side-view and top-view in situ microscopy. Thereafter, mass and momentum transport mathematical models are applied to obtain numerical solutions of transport phenomena within the meniscus. Utilizing these results, the underlying mechanism of dendritic growth of small molecule organic semiconductor is elucidated, which has previously been unknown. The 3D meniscus modeling is particularly important for this analysis, as dendrite formation is strongly dependent on the meniscus geometry near the contact line and mass transport variation perpendicular to the coating direction. This technique enables the study of complex relationship between experimental parameters and solidification process, which is widely applicable to various materials and coating systems; whereby, better understanding of thin-film growth and device performance optimization is possible.