Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral

MSAT: Matrix stability analysis tool for shock-capturing schemes

The simulation of supersonic or hypersonic flows often suffers from numerical shock instabilities if the flow field contains strong shocks, limiting the further application of shock-capturing schemes. In this paper, we develop the unified matrix stability analysis method for schemes with three-point stencils and present MSAT, an open-source tool to quantitatively analyze the shock instability problem. Based on the finite-volume approach on the structured grid, MSAT can be employed to investigate the mechanism of the shock instability problem, evaluate the robustness of numerical schemes, and then help to develop robust schemes. Also, MSAT has the ability to analyze the practical simulation of supersonic or hypersonic flows, evaluate whether it will suffer from shock instabilities, and then assist in selecting appropriate numerical schemes accordingly. As a result, MSAT is a helpful tool that can investigate the shock instability problem and help to cure it.Comment: 18 pages, 6 figure

Learning Dense UV Completion for Human Mesh Recovery

Human mesh reconstruction from a single image is challenging in the presence of occlusion, which can be caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV map dense human with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior SOTA methods on heavily occluded images and achieves comparable results on the standard benchmarks (3DPW)

Diabetes mellitus promotes susceptibility to periodontitis—novel insight into the molecular mechanisms

Diabetes mellitus is a main risk factor for periodontitis, but until now, the underlying molecular mechanisms remain unclear. Diabetes can increase the pathogenicity of the periodontal microbiota and the inflammatory/host immune response of the periodontium. Hyperglycemia induces reactive oxygen species (ROS) production and enhances oxidative stress (OS), exacerbating periodontal tissue destruction. Furthermore, the alveolar bone resorption damage and the epigenetic changes in periodontal tissue induced by diabetes may also contribute to periodontitis. We will review the latest clinical data on the evidence of diabetes promoting the susceptibility of periodontitis from epidemiological, molecular mechanistic, and potential therapeutic targets and discuss the possible molecular mechanistic targets, focusing in particular on novel data on inflammatory/host immune response and OS. Understanding the intertwined pathogenesis of diabetes mellitus and periodontitis can explain the cross-interference between endocrine metabolic and inflammatory diseases better, provide a theoretical basis for new systemic holistic treatment, and promote interprofessional collaboration between endocrine physicians and dentists

Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Scene text detection, an important step of scene text reading systems, has witnessed rapid development with convolutional neural networks. Nonetheless, two main challenges still exist and hamper its deployment to real-world applications. The first problem is the trade-off between speed and accuracy. The second one is to model the arbitrary-shaped text instance. Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications.In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing. More specifically, the segmentation head is made up of Feature Pyramid Enhancement Module (FPEM) and Feature Fusion Module (FFM). FPEM is a cascadable U-shaped module, which can introduce multi-level information to guide the better segmentation. FFM can gather the features given by the FPEMs of different depths into a final feature for segmentation. The learnable post-processing is implemented by Pixel Aggregation (PA), which can precisely aggregate text pixels by predicted similarity vectors. Experiments on several standard benchmarks validate the superiority of the proposed PAN. It is worth noting that our method can achieve a competitive F-measure of 79.9% at 84.2 FPS on CTW1500.Comment: Accept by ICCV 201

Osteoporosis guidelines on TCM drug therapies: a systematic quality evaluation and content analysis

ObjectiveThe aims of this study were to evaluate the quality of osteoporosis guidelines on traditional Chinese medicine (TCM) drug therapies and to analyze the specific recommendations of these guidelines.MethodsWe systematically collected guidelines, evaluated the quality of the guidelines using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool, and summarized the recommendations of TCM drug therapies using the Patient-Intervention-Comparator-Outcome (PICO) model as the analysis framework.Results and conclusionsA total of 20 guidelines were included. Overall quality evaluation results revealed that four guidelines were at level A, four at level B, and 12 at level C, whose quality needed to be improved in the domains of “stakeholder involvement”, “rigor of development”, “applicability” and “editorial independence”. Stratified analysis suggested that the post-2020 guidelines were significantly better than those published before 2020 in the domains of “scope and purpose”, “stakeholder involvement” and “editorial independence”. Guidelines with evidence systems were significantly better than those without evidence systems in terms of “stakeholder involvement”, “rigor of development”, “clarity of presentation” and “applicability”. The guidelines recommended TCM drug therapies for patients with osteopenia, osteoporosis and osteoporotic fracture. Recommended TCM drugs were mainly Chinese patent medicine alone or combined with Western medicine, with the outcome mainly focused on improving bone mineral density (BMD)