Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

From market sensing to new concept development in consultancies:The role of information processing and organizational capabilities

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On the Number of Iterations for Dantzig-Wolfe Optimization and Packing-Covering Approximation Algorithms

We give a lower bound on the iteration complexity of a natural class of Lagrangean-relaxation algorithms for approximately solving packing/covering linear programs. We show that, given an input with $m$ random 0/1-constraints on $n$ variables, with high probability, any such algorithm requires $\Omega(\rho \log(m)/\epsilon^2)$ iterations to compute a $(1+\epsilon)$-approximate solution, where $\rho$ is the width of the input. The bound is tight for a range of the parameters $(m,n,\rho,\epsilon)$. The algorithms in the class include Dantzig-Wolfe decomposition, Benders' decomposition, Lagrangean relaxation as developed by Held and Karp [1971] for lower-bounding TSP, and many others (e.g. by Plotkin, Shmoys, and Tardos [1988] and Grigoriadis and Khachiyan [1996]). To prove the bound, we use a discrepancy argument to show an analogous lower bound on the support size of $(1+\epsilon)$-approximate mixed strategies for random two-player zero-sum 0/1-matrix games

Influence of Pore Size on Carbon Dioxide Diffusion in Two Isoreticular Metal-Organic Frameworks

The rapid diffusion of molecules in porous materials is critical for numerous applications including separations, energy storage, sensing, and catalysis. A common strategy for tuning guest diffusion rates is to vary the material pore size, although detailed studies that isolate the effect of changing this particular variable are lacking. Here, we begin to address this challenge by measuring the diffusion of carbon dioxide in two isoreticular metal-organic frameworks featuring channels with different diameters, Zn2(dobdc) (dobdc4- = 2,5-dioxidobenzene-1,4-dicarboxylate) and Zn2(dobpdc) (dobpdc4- = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate), using pulsed field gradient NMR spectroscopy. An increase in the pore diameter from 15 Å in Zn2(dobdc) to 22 Å in Zn2(dobpdc) is accompanied by an increase in the self-diffusion of CO2 by a factor of 4 to 6, depending on the gas pressure. Analysis of the diffusion anisotropy in Zn2(dobdc) reveals that the self-diffusion coefficient for motion of CO2 along the framework channels is at least 10000 times greater than for motion between the framework channels. Our findings should aid the design of improved porous materials for a range of applications where diffusion plays a critical role in determining performance

Local balancing of the electricity grid in a renewable municipality; analyzing the effectiveness and cost of decentralized load balancing looking at multiple combinations of technologies

With the integration of Intermitted Renewables Energy (I-RE) electricity production, capacity is shifting from central to decentral. So, the question is if it is also necessary to adjust the current load balancing system from a central to more decentral system. Therefore, an assessment is made on the overall effectiveness and costs of decentralized load balancing, using Flexible Renewable Energy (F-RE) in the shape of biogas, Demand Side Management (DSM), Power Curtailment (PC), and electricity Storage (ST) compared to increased grid capacity (GC). As a case, an average municipality in The Netherlands is supplied by 100% I-RE (wind and solar energy), which is dynamically modeled in the PowerPlan model using multiple scenarios including several combinations of balancing technologies. Results are expressed in yearly production mix, self-consumption, grid strain, Net Load Demand Signal, and added cost. Results indicate that in an optimized scenario, self-consumption of the municipality reaches a level of around 95%, the total hours per year production matches demand to over 90%, and overproduction can be curtailed without substantial losses lowering grid strain. In addition, the combination of balancing technologies also lowers the peak load to 60% of the current peak load in the municipality, thereby freeing up capacity for increased demand (e.g., electric heat pumps, electric cars) or additional I-RE production. The correct combination of F-RE and lowering I-RE production to 60%, ST, and PC are shown to be crucial. However, the direct use of DSM has proven ineffective without a larger flexible demand present in the municipality. In addition, the optimized scenario will require a substantial investment in installations and will increase the energy cost with 75% in the municipality (e.g., from 0.20€ to 0.35€ per kWh) compared to 50% (0.30€ per kWh) for GC. Within this context, solutions are also required on other levels of scale (e.g., on middle or high voltage side or meso and macro level) to ensure security of supply and/or to reduce overall costs

Mapping the Early Cortical Folding Process in the Preterm Newborn Brain

In the developing human brain, the cortical sulci formation is a complex process starting from 14 weeks of gestation onward. The potential influence of underlying mechanisms (genetic, epigenetic, mechanical or environmental) is still poorly understood, because reliable quantification in vivo of the early folding is lacking. In this study, we investigate the sulcal emergence noninvasively in 35 preterm newborns, by applying dedicated postprocessing tools to magnetic resonance images acquired shortly after birth over a developmental period critical for the human cortex maturation (26-36 weeks of age). Through the original three-dimensional reconstruction of the interface between developing cortex and white matter and correlation with volumetric measurements, we document early sulcation in vivo, and quantify changes with age, gender, and the presence of small white matter lesions. We observe a trend towards lower cortical surface, smaller cortex, and white matter volumes, but equivalent sulcation in females compared with males. By precisely mapping the sulci, we highlight interindividual variability in time appearance and interhemispherical asymmetries, with a larger right superior temporal sulcus than the left. Thus, such an approach, included in a longitudinal follow-up, may provide early indicators on the structural basis of cortical functional specialization and abnormalities induced by genetic and environmental factor