CT Image Segmentation Using FEM with Optimized Boundary Condition

The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery

High-density single-molecule maps reveal transient membrane receptor interactions within a dynamically varying environment

Over recent years, super-resolution and single-molecule imaging methods have delivered unprecedented details on the nanoscale organization and dynamics of individual molecules in different contexts. Yet, visualizing single-molecule processes in living cells with the required spatial and temporal resolution remains highly challenging. Here, we report on an analytical approach that extracts such information from live-cell single-molecule imaging at high-labeling densities using standard fluorescence probes. Our high-density-mapping (HiDenMap) methodology provides single-molecule nanometric localization accuracy together with millisecond temporal resolution over extended observation times, delivering multi-scale spatiotemporal data that report on the interaction of individual molecules with their dynamic environment. We validated HiDenMaps by simulations of Brownian trajectories in the presence of patterns that restrict free diffusion with different probabilities. We further generated and analyzed HiDenMaps from single-molecule images of transmembrane proteins having different interaction strengths to cortical actin, including the transmembrane receptor CD44. HiDenMaps uncovered a highly heterogenous and multi-scale spatiotemporal organization for all the proteins that interact with the actin cytoskeleton. Notably, CD44 alternated between periods of random diffusion and transient trapping, likely resulting from actin-dependent CD44 nanoclustering. Whereas receptor trapping was dynamic and lasted for hundreds of milliseconds, actin remodeling occurred at the timescale of tens of seconds, coordinating the assembly and disassembly of CD44 nanoclusters rich regions. Together, our data demonstrate the power of HiDenMaps to explore how individual molecules interact with and are organized by their environment in a dynamic fashion.Comment: 33 pages, 5 figure

Quantifying the effects of mass transport in the curing and leaching of agglomerated ores using X-ray Microtomography

Agglomeration and subsequent curing are widely used as pre-treatments for ore prior to heap leaching as they both improve the permeability of the heap and bring leaching solution into close contact with the ore, initializing the leaching reactions. In this thesis, a low-grade copper sulphide ore was used for the experiments and two different agglomeration/leaching solutions were tested, namely a more standard sulphuric acid solution including ferric/ferrous ions, and a solution which also contained chloride ions. A novel image processing methodology was developed to track grains over both the curing and leaching process, taking into account the anisometric changes experienced by the agglomerates and the formation and depletion of species. A combination of XMT and SEM/EDX was used to characterise the chemical and mineralogical changes occurring over both processes. The formation and depletion of mineral components were quantified and tracked beyond the typical time scales used industrially, highlighting that the presence of chloride ions makes a substantial difference to the chemical and structural evolution of the agglomerates. Over the curing process, at least 20 days are required to perceive a significant degree of dissolution. Reprecipitation of metal containing species was observed, especially near the agglomerate surfaces. These precipitates are water-soluble species, and 50% of the initial sulphides were extracted from the agglomerates containing chloride ions, but only 20% from the other agglomerates after curing and water washing. A model of the agglomerate behaviour over the curing process is proposed based on the results observed from the XMT measurements. This model considers both the metal dissolution extent, as well as the reprecipitation of species due to water evaporation. The mathematical model is explained together with the computational approach used to solve it, and the simulation results are compared with the experimental results. This model is able to successfully predict the trends seen in the experiments, with the relative reaction and evaporation rates being a controlling factor. The leach performance was assessed for agglomerates leached using the same recipes used for the agglomeration stage. The compaction and changes in microporosity in the sample were quantified, showing that these changes do not significantly influence the leaching performance. By taking advantage of the more selective leaching that takes place when chloride ions are added to the leach solution, the leaching variability in the system was assessed. SEM/EDX measurements were then used to calibrate the XMT quantifications, isolating the dissolution of copper-containing grains from the pyrite dissolution. It was, thus, possible to quantify the surface kinetics of the hundreds of thousands of grains in the sample, with these kinetics being represented by a family of bi-modal curves. It was shown that the mass transport and mineralogical changes occurring throughout the curing and leaching processes could be quantified both at the grain-scale and the macro-scale by using the developed methodology for combining SEM/EDX measurements with XMT. By incorporating this data into particle scale and, ultimately, heap scale leach models, improved predictions and optimisation of leach performance can be made.Open Acces