Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data

An evaluation of the Swiss staging model for hypothermia using case reports from the literature.

BACKGROUND: Core body temperature is used to stage and guide the management of hypothermic patients, however obtaining accurate measurements of core temperature is challenging, especially in the pre-hospital context. The Swiss staging model for hypothermia uses clinical indicators to stage hypothermia. The proposed temperature range for clinical stage 1 is <35-32 °C (95-90 °F), for stage 2, <32-28 °C (<90-82 °F) for stage 3, <28-24 °C (<82-75 °F), and for stage 4 below 24 °C (75 °F). However, the evidence relating these temperature ranges to the clinical stages needs to be strengthened. METHODS: Medline was used to retrieve data on as many cases of accidental hypothermia (core body temperature <35 °C (95 °F)) as possible. Cases of therapeutic or neonatal hypothermia and those with confounders or insufficient data were excluded. To evaluate the Swiss staging model for hypothermia, we estimated the percentage of those patients who were correctly classified and compared the theoretical with the observed ranges of temperatures for each clinical stage. The number of rescue collapses was also recorded. RESULTS: We analysed 183 cases; the median temperature for the sample was 25.2 °C (IQR 22-28). 95 of the 183 patients (51.9%; 95% CI = 44.7%-59.2%) were correctly classified, while the temperature was overestimated in 36 patients (19.7%; 95% CI = 13.9%-25.4%). We observed important overlaps among the four stage groups with respect to core temperature, the lowest observed temperature being 28.1 °C for Stage 1, 22 °C for Stage 2, 19.3 °C for Stage 3, and 13.7 °C for stage 4. CONCLUSION: Predicting core body temperature using clinical indicators is a difficult task. Despite the inherent limitations of our study, it increases the strength of the evidence linking the clinical hypothermia stage to core temperature. Decreasing the thresholds of temperatures distinguishing the different stages would allow a reduction in the number of cases where body temperature is overestimated, avoiding some potentially negative consequences for the management of hypothermic patients

Outcome prediction for hypothermic patients in cardiac arrest.

The 5A score predicts in-hospital mortality of patients suffering from accidental hypothermia, including those not in cardiac arrest. The HOPE score was specifically developed to predict survival for the subgroup of hypothermic patients in cardiac considered for extracorporeal life support rewarming. The C-statistic in the external validation study of the HOPE score was 0.825 (95% CI: 0.753-0.897), confirming its excellent discrimination. In addition, its good calibration allows for a reliable interpretation of the corresponding survival probability after rewarming. The HOPE score should be used for predicting outcome and selecting hypothermic patients in cardiac arrest for rewarming

An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images.

This paper addresses the segmentation from an image of entities that have the form of a 'network', i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present a new phase field higher-order active contour (HOAC) prior model for network regions, and apply it to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much 'noise' in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a severe limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly Curved, wide branches. To solve this problem, we propose a new HOAC prior energy term, and reformulate it as a nonlocal phase field energy. We analyse the stability of the new model, and find that in addition to solving the above problem by separating the interactions between points on the same and opposite sides of a network branch, the new model permits the modelling of two widths simultaneously. The analysis also fixes some of the model parameters in terms of network width(s). After adding a likelihood energy, we use the model to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The results demonstrate the superiority of the new model, the importance of strong prior knowledge in general, and of the new term in particular

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images

Validation of the Chinese Version of the Problem Areas in Diabetes (PAID-C) Scale

OBJECTIVE To examine the psychometric properties of a Chinese version of the Problem Areas In Diabetes (PAID-C) scale.\ud \ud RESEARCH DESIGN AND METHODS The reliability and validity of the PAID-C were evaluated in a convenience sample of 205 outpatients with type 2 diabetes. Confirmatory factor analysis, Bland-Altman analysis, and Spearman's correlations facilitated the psychometric evaluation.\ud \ud RESULTS Confirmatory factor analysis confirmed a one-factor structure of the PAID-C (χ2/df ratio = 1.894, goodness-of-fit index = 0.901, comparative fit index = 0.905, root mean square error of approximation = 0.066). The PAID-C was associated with A1C (rs = 0.15; P < 0.05) and diabetes self-care behaviors in general diet (rs = −0.17; P < 0.05) and exercise (rs = −0.17; P < 0.05). The 4-week test-retest reliability demonstrated satisfactory stability (rs = 0.83; P < 0.01).\ud \ud CONCLUSIONS The PAID-C is a reliable and valid measure to determine diabetes-related emotional distress in Chinese people with type 2 diabetes

Normal gas exchange after 30-h ischemia and treatment with phosphodiesterase inhibitor PDI747

Objective: Phosphodiesterases (PDEs) negatively regulate the concentrations of cAMP and/or cGMP, which act as downstream second messengers to the prostaglandins. PDE type-4 (PDE4) is selective for cAMP and is found in high concentrations in endothelial, epithelial, and different blood cells. The aim of this study was to evaluate if PDI747, a novel selective inhibitor of PDE4, can restore pretransplant cAMP levels and thereby posttransplant organ function after prolonged cold ischemia. Methods: Left lung transplantation was performed in pigs (25-31 kg). Donor lungs were flushed with low potassium dextran glucose (LPDG) solution only (control, n=5)or, in addition with 1 μmol of PDI747 (PDI747, n=5) and stored for 30 h at 1 °C. PDI747 animals further received a bolus of PDI747 (0.3 mg/kg) 15 min prior to reperfusion and a continuous infusion (0.3 mg/kg per hour) during the 5 h after reperfusion. After occlusion of the right pulmonary arteries and the right main bronchus, hemodynamic and gas exchange parameters and extravascular lung water (EVLW) levels of the transplanted lung were assessed. Results: Two control animals died of severe lung edema leading to heart failure (control, n=3). One animal in the treatment group was excluded due to a patent ductus arteriosus (PDI747, n=4). Gas exchange at the end of the experiment was restored to normal levels in the PDI747 group (Pa, o2 47.6±11.2 kPa, Pa,co2 6.4±1.8 kPa) but not in the control group (Pa, o2 7.7±2.9 kPa, Pa, co2 11.9±3.0 kPa, PPao2<0.0001, PPa, co2=0.06). Extravascular lung water (EVLW) was normal in the PDI747 group (8.5±1.1 ml/kg) and clearly elevated in the control group (16.2±5.6 ml/kg, P=0.007). Airway pressure in the PDI747 group was significantly lower than in the control group (7.8±0.5 cm H2O vs. 11.3±0.6 cm H2O, respectively, P<0.0001). The free radical mediated tissue injury measured by lipid peroxidation (TBARS) was significantly reduced (P=0.001) in the PDI747 group. Conclusions: With the inhibition of PDE4 with PDI747 we achieved normal gas exchange, no posttransplant lung edema, normal airway pressures, and a reduced free radical injury after 30 h of cold ischemi

Association of plasma zinc levels with anti-SARS-CoV-2 IgG and IgA seropositivity in the general population: A case-control study.

Some micronutrients have key roles in immune defence, including mucosal defence mechanisms and immunoglobulin production. Altered micronutrient status has been linked with COVID-19 infection and disease severity. We assessed the associations of selected circulating micronutrients with anti-SARS-CoV-2 IgG and IgA seropositivity in the Swiss community using early pandemic data. Case-control study comparing the first PCR-confirmed COVID-19 symptomatic cases in the Vaud Canton (May to June 2020, n = 199) and controls (random population sample, n = 447), seronegative for IgG and IgA. The replication analysis included seropositive (n = 134) and seronegative (n = 152) close contacts from confirmed COVID-19 cases. Anti-SARS-CoV-2 IgG and IgA levels against the native trimeric spike protein were measured using the Luminex immunoassay. We measured plasma Zn, Se and Cu concentrations by ICP-MS, and 25-hydroxy-vitamin D &lt;sub&gt;3&lt;/sub&gt; (25(OH)D &lt;sub&gt;3&lt;/sub&gt; ) with LC-MS/MS and explored associations using multiple logistic regression. The 932 participants (54.1% women) were aged 48.6 ± 20.2 years (±SD), BMI 25.0 ± 4.7 kg/m &lt;sup&gt;2&lt;/sup&gt; with median C-Reactive Protein 1 mg/l. In logistic regressions, log &lt;sub&gt;2&lt;/sub&gt; (Zn) plasma levels were negatively associated with IgG seropositivity (OR [95% CI]: 0.196 [0.0831; 0.465], P &lt; 0.001; replication analyses: 0.294 [0.0893; 0.968], P &lt; 0.05). Results were similar for IgA. We found no association of Cu, Se, and 25(OH)D &lt;sub&gt;3&lt;/sub&gt; with anti-SARS-CoV-2 IgG or IgA seropositivity. Low plasma Zn levels were associated with higher anti-SARS-CoV-2 IgG and IgA seropositivity in a Swiss population when the initial viral variant was circulating, and no vaccination available. These results suggest that adequate Zn status may play an important role in protecting the general population against SARS-CoV-2 infection. CORONA IMMUNITAS:: ISRCTN18181860

Secular trends in motor performance in Swiss children and adolescents from 1983 to 2018

Introduction: Environmental changes, including globalization, urbanization, social and cultural changes in society, and exposure to modern digital technology undoubtedly have an impact on children's activity and lifestyle behavior. In fact, marked reductions in children's physical activity levels have been reported over the years and sedentary behavior has increased around the world. The question arises whether these environmental changes had an impact on general motor performance in children and adolescents. The study aimed to investigate secular trends of motor performance in Swiss children and adolescents, aged between 7 and 18 years, over a period of 35 years from 1983 to 2018. Methods: Longitudinal data on the five motor components of the Zurich Neuromotor Assessment (ZNA) - pure motor (PM), fine motor (FM), dynamic balance (DB), static balance (SB), and contralateral associated movements (CAM) - were pooled with cross-sectional data on PM and FM from eight ZNA studies between 1983 and 2018. Regression models were used to estimate the effect of the year of birth on motor performance and body mass index (BMI) measurements. Models were adjusted for age, sex, and socioeconomic status. Results: The secular trend estimates in standard deviation scores (SDS) per 10 years were - 0.06 [-0.33; 0.22, 95% Confidence Interval] for PM, -0.11 [-0.41; 0.20] for FM, -0.38 [-0.66; -0.09] for DB (-0.42 when controlled for BMI), -0.21 [-0.47; 0.06] for SB, and - 0.01 [-0.32; 0.31] for CAM. The mean change in BMI data was positive with 0.30 SDS [0.07; 0.53] over 10 years. Discussion: Despite substantial societal changes since the 1980s, motor performance has remained relatively stable across generations. No secular trend was found in FM, PM, SB, and CAM over a period of 35 years. A secular trend in DB was present independent of the secular trend in body mass index

A multi-layer 'gas of circles' Markov random field model for the extraction of overlapping near-circular objects.

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images