Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction

Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases. We explore deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Methods: We trained a U-Net for artefact reduction on simulated sparse-view cranial CT scans from 3000 patients obtained from a public dataset and reconstructed with varying levels of sub-sampling. Additionally, we trained a convolutional neural network on fully sampled CT data from 17,545 patients for automated hemorrhage detection. We evaluated the classification performance using the area under the receiver operator characteristic curves (AUC-ROCs) with corresponding 95% confidence intervals (CIs) and the DeLong test, along with confusion matrices. The performance of the U-Net was compared to an analytical approach based on total variation (TV). Results: The U-Net performed superior compared to unprocessed and TV-processed images with respect to image quality and automated hemorrhage diagnosis. With U-Net post-processing, the number of views can be reduced from 4096 (AUC-ROC: 0.974; 95% CI: 0.972-0.976) views to 512 views (0.973; 0.971-0.975) with minimal decrease in hemorrhage detection (P<.001) and to 256 views (0.967; 0.964-0.969) with a slight performance decrease (P<.001). Conclusion: The results suggest that U-Net based artifact reduction substantially enhances automated hemorrhage detection in sparse-view cranial CTs. Our findings highlight that appropriate post-processing is crucial for optimal image quality and diagnostic accuracy while minimizing radiation dose.Comment: 11 pages, 6 figures, 1 tabl

Strengthening Integrated Primary Health Care in Sofala, Mozambique

Background: Large increases in health sector investment and policies favoring upgrading and expanding the public sector health network have prioritized maternal and child health in Mozambique and, over the past decade, Mozambique has achieved substantial improvements in maternal and child health indicators. Over this same period, the government of Mozambique has continued to decentralize the management of public sector resources to the district level, including in the health sector, with the aim of bringing decision-making and resources closer to service beneficiaries. Weak district level management capacity has hindered the decentralization process, and building this capacity is an important link to ensure that resources translate to improved service delivery and further improvements in population health. A consortium of the Ministry of Health, Health Alliance International, Eduardo Mondlane University, and the University of Washington are implementing a health systems strengthening model in Sofala Province, central Mozambique.Description of implementation: The Mozambique Population Health Implementation and Training (PHIT) Partnership focuses on improving the quality of routine data and its use through appropriate tools to facilitate decision making by health system managers; strengthening management and planning capacity and funding district health plans; and building capacity for operations research to guide system-strengthening efforts. This seven-year effort covers all 13 districts and 146 health facilities in Sofala Province.Evaluation design: A quasi-experimental controlled time-series design will be used to assess the overall impact of the partnership strategy on under-5 mortality by examining changes in mortality pre- and post-implementation in Sofala Province compared with neighboring Manica Province. The evaluation will compare a broad range of input, process, output, and outcome variables to strengthen the plausibility that the partnership strategy led to healthsystem improvements and subsequent population health impact.Discussion: The Mozambique PHIT Partnership expects to provide evidence on the effect of efforts to improvedata quality coupled with the introduction of tools, training, and supervision to improve evidence-based decision making. This contribution to the knowledge base on what works to enhance health systems is highly replicable for rapid scale-up to other provinces in Mozambique, as well as other sub-Saharan African countries with limitedresources and a commitment to comprehensive primary health care

Microstructuring of Steel and Hard Metal using Femtosecond Laser Pulses

AbstractNew results on three-dimensional micro-structuring of tungsten carbide hard metal and steel using femtosecond laser pulses will be presented. For the investigations, a largely automated high-precision fs-laser micromachining station was used. The fs-laser beam is focused onto the sample surface using different objectives. The investigations of the ablation behaviour of the various materials in dependence of the laser processing parameters will be presented. In the second part, complex 3D microstructures with a variety of geometries and resolutions down to a few micrometers will be presented. On of the Goal of these investigations was to create defined microstructures in tooling equipments such as cutting inserts

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and P-values calculated from one-sided Mann-Whitney U-test were utilized to compare model variations. Results: Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89] (P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range

Photoconductivity in AC-driven modulated two dimensional electron gas in a perpendicular magnetic field

In this work we study the microwave photoconductivity of a two-dimensional electron system (2DES) in the presence of a magnetic field and a two-dimensional modulation (2D). The model includes the microwave and Landau contributions in a non-perturbative exact way, the periodic potential is treated perturbatively. The Landau-Floquet states provide a convenient base with respect to which the lattice potential becomes time-dependent, inducing transitions between the Landau-Floquet levels. Based on this formalism, we provide a Kubo-like formula that takes into account the oscillatory Floquet structure of the problem. The total longitudinal conductivity and resistivity exhibit strong oscillations, determined by $\epsilon = \omega / \omega_c$ with $\omega$ the radiation frequency and $\omega_c$ the cyclotron frequency. The oscillations follow a pattern with minima centered at $\omega/\omega_c =j + {1/2} (l-1) + \delta$, and maxima centered at $\omega/\omega_c =j + {1/2} (l-1) - \delta$, where $j=1,2,3.......$, $\delta \sim 1/5$ is a constant shift and $l$ is the dominant multipole contribution. Negative resistance states (NRS) develop as the electron mobility and the intensity of the microwave power are increased. These NRS appear in a narrow window region of values of the lattice parameter ($a$), around $a \sim l_B$, where $l_B$ is the magnetic length. It is proposed that these phenomena may be observed in artificially fabricated arrays of periodic scatterers at the interface of ultraclean $GaAs/Al_xGa_{1-x} As$ heterostructures.Comment: 20 pages, 8 figure

Francy - an interactive discrete mathematics framework for GAP

Funding: European Union project Open Digital Research Environment Toolkit for the Advancement of Mathematics (EC Horizon 2020 project 676541, 01/09/2015-31/08/2019).Data visualization and interaction with large data sets is known to be essential and critical in many businesses today, and the same applies to research and teaching, in this case, when exploring large and complex mathematical objects. GAP is a computer algebra system for computational discrete algebra with an emphasis on computational group theory. The existing XGAP package for GAP works exclusively on the X Window System. It lacks abstraction between its mathematical and graphical cores, making it difficult to extend, maintain, or port. In this paper, we present Francy, a graphical semantics package for GAP. Francy is responsible for creating a representational structure that can be rendered using many GUI frameworks independent from any particular programming language or operating system. Building on this, we use state of the art web technologies that take advantage of an improved REPL environment, which is currently under development for GAP. The integration of this project with Jupyter provides a rich graphical environment full of features enhancing the usability and accessibility of GAP.Postprin

Towards the conservation of Borneo’s freshwater mussels: rediscovery of the endemic Ctenodesma borneensis and first record of the non-native Sinanodonta lauta

The freshwater mussel fauna of Borneo is highly endemic, with at least 11 species being unique to that island. Most of these species have not been recorded for at least 50 years owing to a lack of sampling effort and large-scale habitat destruction and degradation. Surveys conducted in 2016 across much of Malaysian Borneo failed to locate four out of five native species historically recorded in the study area. The present study aimed to determine the diversity and distribution of freshwater mussels of Brunei and adjacent Limbang Division, Malaysia. In 2018, we conducted interviews with locals, recorded environmental data and surveyed mussels at 43 sites, and conducted interviews at a further 38 sites. Only one population of native mussels, i.e. Ctenodesma borneensis, was found in a small tributary of the Limbang River situated in a patch of intact rainforest, representing the first record of this Bornean endemic genus since 1962. In addition, Sinanodonta lauta was found in a pond in Lawas district, representing the first record of this species outside its native East Asian distribution. Our data suggest that C. borneensis can sustain populations in relatively undisturbed habitats and is likely to have suffered population losses across northern Borneo. The first molecular phylogenetic analysis (COI + 28S) including an endemic Bornean freshwater mussel genus revealed that Ctenodesma is phylogenetically divergent from all other previously sampled lineages, rendering it a particularly valuable conservation target