Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

Emergent properties of microbial activity in heterogeneous soil microenvironments:Different research approaches are slowly converging, yet major challenges remain

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the usual bulk, macroscopic parameters used to characterize soils (e.g., granulometry, pH, soil organic matter and biomass contents) provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gases. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale). For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. In terms of microbial aspects, whereas a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because relevant experimental data are extremely scarce. For the overall research to move forward, it will be crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead

Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

Percutaneous thermal ablation has proved to be an effective modality for treating both benign and malignant tumors in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50 oC, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumor destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of- the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non- invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow

Chemical analysis of polymer blends via synchrotron X-ray tomography

Material properties of industrial polymer blends are of great importance. X-ray tomography has been used to obtain spatial chemical information about various polymer blends. The spatial images are acquired with synchrotron X-ray tomography because of its rapidity, good spatial resolution, large field-of-view, and elemental sensitivity. The spatial absorption data acquired from X-ray tomography experiments is converted to spatial chemical information via a linear least squares fit of multi-spectral X-ray absorption data. A fiberglass-reinforced polymer blend with a new-generation flame retardant is studied with multi-energy synchrotron X-ray tomography to assess the blend homogeneity. Relative to other composite materials, this sample is difficult to image due to low x-ray contrast between the fiberglass reinforcement and the polymer blend. To investigate chemical composition surrounding the glass fibers, new procedures were developed to find and mark the fiberglass, then assess the flame retardant distribution near the fiber. Another polymer blending experiment using three-dimensional chemical analysis techniques to look at a polymer additive problem called blooming was done. To investigate the chemical process of blooming, new procedures are developed to assess the flame retardant distribution as a function of annealing time in the sample. With the spatial chemical distribution we fit the concentrations to a diffusion equation to each time step in the annealing process. Finally the diffusion properties of a polymer blend composed of hexabromobenzene and o-terphenyl was studied. The diffusion properties were compared with computer simulations of the blend

Imaging dental ultrasonic cavitation and its effects

Current methods of dental biofilm removal are predominantly mechanical and are not effective in removing it from irregular surfaces in the mouth. Cavitation occurs around dental ultrasonic scalers and may be a more efficient and less damaging technique. Previous work has failed to quantify the cavitation bubble dynamics around ultrasonic scalers and its effects. The aim was to develop imaging and analysis protocols to analyse the cavitation and to investigate its ability to disrupt biofilms and deliver sub-micron particles into dentine. High speed imaging was used to characterise cavitation. Its effect on biofilm removal and dentinal tubule occlusion was studied using electron microscopy and x-ray micro computed tomography. We are able to demonstrate that cavitation occurs at the free end of scaler tips and increases with power and vibration amplitude. Biofilm can effectively be removed from dental implant surfaces using this cavitation. It can also be used to transport sub-micron particles further into dentinal tubules. The results show that ultrasonic scalers could be optimised for non-contact use and improved removal of plaque from the teeth. The protocols established in this study can be applied to future studies for quantitative investigation of biofilm growth and removal and analysis of cavitation dynamics

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Use of X-ray K-edge Tomography and Interferometry Imaging Techniques for the Studies of Brominated Flame Retardants

The work presented in this dissertation is based on the studies of flame retardancy performance of various formulations consisting of brominated flame retardants (BFRs: Saytex 8010 and Green Armor) and their synergist, antimony trioxide (Sb2O3) in high impact polystyrene (HIPS). Chemical flame retardants are incorporated in polymers to improve their flame inhibition for optimal applications in electrical and electronic devices, furniture, printers and more. These flame retardant polymer blends are studied using the Underwriters Laboratory vertical burn test (UL 94) and X-ray imaging techniques such as X-ray K-edge absorption tomography and X-ray grating interferometry. The UL 94 burn test is initially performed to assess the flammability behavior of flame retardant samples before X-ray imaging methods of burnt and pristine polymer blends. Because the UL 94 test bars are formulated with varying concentrations of a brominated flame retardant (Saytex 8010® or Green Armor®) and a synergist, Sb2O3 into a high impact polystyrene (HIPS), samples pass or fail the UL 94 plastics flammability test based on the burn time and other factors. Then, the X-ray imaging techniques are used to reveal internal features for the flame retardant performance during the burn. The Underwriters Laboratory 94 test bars are imaged with X-ray K-edge absorption tomography between 12 to 32 keV to assess the bromine and antimony concentration gradient across char layers of partially burnt samples. X-ray grating interferometry on partially burnt samples shows gas bubbles and dark-field scattering ascribed to residual blend inhomogeneity. In addition, X-ray single-shot grating interferometry is used to record X-ray movies of test samples during heating intended to mimic the UL 94 plastics flammability test. Key features such as char layer, gas bubble formation, micro-cracks, and dissolution of the flame retardant in the char layer regions are used in understanding the efficiency of the flame retardant and synergist. The samples that pass the UL 94 test have a thick, highly visible char layer, low bromine and antimony concentration in the char layer as well as an interior rich in gas bubbles. Growth of gas bubbles from flame retardant thermal decomposition is noted in the X-ray phase contrast movies