Hierarchical Framework for Automatic Pancreas Segmentation in MRI Using Continuous Max-flow and Min-Cuts Approach

Accurate, automatic and robust segmentation of the pancreas in medical image scans remains a challenging but important prerequisite for computer-aided diagnosis (CADx). This paper presents a tool for automatic pancreas segmentation in magnetic resonance imaging (MRI) scans. Proposed is a framework that employs a hierarchical pooling of information as follows: identify major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; perform 3D segmentation by employing continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, curvature and position between distinct contours. The proposed method is evaluated on a dataset of 20 MRI volumes, achieving a mean Dice Similarity coefficient of 75.5 ± 7.0% and a mean Jaccard Index coefficient of 61.2 ± 9.2%

Lagrange's discrete model of the wave equation in dimension greater than one

A celebrated theorem of Lagrange states that a solution of the wave equation with one-dimensional space variable is the uniform limit, as N tends to infinity, of a second order ODE obtained from a mechanical model discretizing a string as N identical harmonic oscillators. Answering to a question posed by G. Gallavotti we generalize this result to the case of any space dimension

AI Driven IoT Web-Based Application for Automatic Segmentation and Reconstruction of Abdominal Organs from Medical Images

Medical imaging technology has rapidly advanced in the last few decades, providing detailed images of the human body. The accurate analysis of these images and the segmentation of anatomical structures can produce significant morphological information, provide additional guidance toward subject stratification after diagnosis or before a clinical trial, and help predict a medical condition. Usually, medical scans are manually segmented by expert operators, such as radiologists and radiographers, which is complex, time-consuming and prone to inter-observer variability. A system that generates automatic, accurate quantitative organ segmentation on a large scale could deliver a clinical impact, supporting current investigations in subjects with medical conditions and aiding early diagnosis and treatment planning. This paper proposes a web-based application that automatically segments multiple abdominal organs and muscle, produces respective 3D reconstructions and extracts valuable biomarkers using a deep learning backend engine. Furthermore, it is possible to upload image data and access the medical image segmentation tool without installation using any device connected to the Internet. The final aim is to deliver a web- based image-processing service that clinical experts, researchers and users can seamlessly access through IoT devices without requiring knowledge of the underpinning technology

Experimental tests of solar collectors prototypes systems

Solar thermal collectors represent one of the most widely used technologies for heat production from renewable energy sources. To increase efficiency and to not increase too much cost different type of solar collectors, and in particular of evacuated tube collectors have been realized. In order to compare performance, tests at different conditions and in different configurations have to be performed. The aim of this paper is to establish the performance of a new prototype via an experimental evaluation of the performance in different conditions and configurations of three collectors. The prototype is particular owing to his new head configuration that permits an innovative parallel configuration way. Therefore, parallel and series configurations have been analyzed applying the UNI-EN 12975, in a steady-state regime. The efficiencies of the two configurations have been tested for different flow rates and different inflow water temperatures. The experimental results show that, with the same input flow rate to the single collector, the parallel configuration has higher performance than the series one, reaching 15% higher level of efficiency. Thus, it seems that these prototypes in optimized configuration can lead to a systems improvement, thereby increasing the overall energy production or giving the same energy production with smaller collector area. © 2015 Published by Elsevier Ltd

Techno-economic analysis of hydrogen production using biomass gasification. A small scale power plant study

Hydrogen has the potential to be a clean alternative to the fossil fuels currently used. This is especially true if hydrogen is manufactured from renewable resources such as biomass. However, hydrogen from biomass faces techno and economic challenges especially in the small size required for the decentralized hydrogen production. In this purpose, a techno economic analysis was carried out on small scale (100kWth) system. The plant is mainly composed of gasifier (double bubbling fluidized bed reactor) coupled with a Portable Purification Unit (PPS: catalytic filter candles, Water Gas Shift and Pressure Swing Absorption). This work focuses on system costs to identify barriers to the development of this technology. A sensitivity analysis was conducted to study hydrogen production cost as a function of capital cost, operating cost and hydrogen production efficiency. The results showed that although efficiency of the production system is the main factor to fall production cost, it cannot be able to reduce costs to favorable level alone. In other words, PPS cost recognized as the major cost is requisite to go down. Therefore, the 50% reduction of PPS cost and the variation of steam to biomass from 1 to 1.5 allow the special cost to fluctuate between 12.75-9.5 €/kg

Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation

Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches

Energy and economic analysis of a residential Solar Organic Rankine plant

To answer the actual energy, water, economic, social and environmental challenges, renewable, distributed power plants need to be developed. Among renewables, solar tri-generative power plants can be a solution where there is big low temperature heating/cooling demand and small electricity demand, like many residential and industrial utilities. In this case, solar thermal plants can produce thermal energy with low cost and high efficiency. The higher temperature heat not needed by the user can be exploited via Organic Rankine Cycle to produce electrical energy and desalinized water via reverse osmosis. The present paper analyses, via TRNSYS simulation, a system composed of 50 m2 of CPC solar thermal collectors, 3 m3 of thermal storage, a synthetic heat transfer fluid, 3 kWe ORC, 8 kWth absorber, 200 l/h direct reverse osmosis desalination device. The system is able to produce power, heating/cooling and fresh water needs for a residential house. Although system’s components are well known technologies, the integration to a efficient and economic working system is still a challenge. Global energy and economic analyses have been performed. Low temperature heating/cooling terminals allow to increase not only the use of thermal energy but also the ORCand absorber efficiency. ORC-Absorber configuration and relative fluids and temperatures are central. Government support and/or cost reduction of 30% are necessary to have positive NPV and acceptable PBT and IR

Special Attention to Physical Activity in Breast Cancer Patients during the First Wave of COVID-19 Pandemic in Italy: The DianaWeb Cohort

Recent evidence highlights that physical activity (PA) is associated with decreased recurrence risk, improved survival and quality of life for breast cancer (BC) patients. Our study aimed to explore patterns of increased/decreased PA, and sedentary behaviors among BC women of the DianaWeb cohort during the first wave of COVID-19 pandemic, and examined the association with residential locations, work changes, different modality used to increase PA, and quality of life. The study analyzed the questionnaires completed by the 781 BC women (age 54.68 ± 8.75 years on both December 2019 and June 2020. Results showed a decrease of 22%, 57%, and 26% for walking activity, vigorous activity, and total PA, respectively. Sitting/lying time increased up to 54.2% of the subjects recruited. High quality of life was associated with lower odds of being sedentary (p = 0.003). Our findings suggest that innovative health management fostering compliance with current guidelines for PA and active behavior should be implemented, especially in unpredictable emergency conditions

Evaluating the drivers of seasonal streamflow in the U.S. Midwest

Streamflows have increased notably across the U.S. Midwest over the past century, fueling a debate on the relative influences of changes in precipitation and land cover on the flow distribution. Here we propose a simple modeling framework to evaluate the main drivers of streamflow rates. Streamflow records from 290 long-term USGS stream gauges were modeled using five predictors: precipitation, antecedent wetness, temperature, agriculture, and population density. We evaluated which predictor combinations performed best for every site, season and streamflow quantile. The goodness-of-fit of our models is generally high and varies by season (higher in the spring and summer than in the fall and winter), by streamflow quantile (best for high flows in the spring and winter, for low flows in the fall, and good for all flow quantiles in summer), and by region (better in the southeastern Midwest than in the northwestern Midwest). In terms of predictors, we find that precipitation variability is key for modeling high flows, while antecedent wetness is a crucial secondary driver for low and median flows. Temperature improves model fits considerably in areas and seasons with notable snowmelt or evapotranspiration. Last, in agricultural and urban basins, harvested acreage and population density are important predictors of changing streamflow, and their influence varies seasonally. Thus, any projected changes in these drivers are likely to have notable effects on future streamflow distributions, with potential implications for basin water management, agriculture, and flood risk management