Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050.Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively.In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.Bill & Melinda Gates Foundation.Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.

The purpose of this study was to evaluate adipose tissue distributions and hepatic and pancreatic fat contents using a 6-point Dixon MRI technique in type 2 diabetes mellitus (T2DM), and to assess associations between fat distributions and biochemical markers of insulin resistance. Intra-abdominal MRI was investigated in 14 T2DM patients, 13 age- and sex-matched healthy controls (HC) and 11 young HC using a 3 T Prisma MRI scanner. All T2DM subjects completed a fasting comprehensive metabolic panel, and demographic measurements were taken according to standardized methodologies. We observed excellent correlation (R = 0.94) between hepatic fat fraction quantified using 6-point Dixon MRI and gold standard MRS, establishing the accuracy and reliability of the Dixon technique. Significantly increased visceral adipose tissue (VAT) volumes were found in T2DM patients compared to age-matched HC (1569.81 ± 670.62 cm vs. 1106.60 ± 566.85 cm, p = .04). We also observed a trend of increasing subcutaneous adipose tissues (SAT), and total abdominal fat (TAT) volumes in T2DM compared to age-matched HC. Hepatic fat fraction percentage (HFF%) was 44.6% higher in T2DM compared to age-matched HC and 64.4% higher compared to young HC. Pancreatic fat fractions in the head and body/tail were higher in T2DM patients compared to both healthy cohorts. We also observed correlations between fat contents of the liver and pancreas in T2DM patients, and association between biochemical markers of T2DM with HFF, indicating a risk for non-alcoholic fatty liver disease among T2DM. In summary, this study provides evidence of T2DM patients having increased liver and pancreatic fat, as well as increased adipose tissues.Copyright © 2020 Elsevier Inc. All rights reserved.

To determine the association between regional fat content in the pancreas and the presence or absence of type II diabetes mellitus (T2DM), the value of regional pancreatic fat quantification in identifying patients at risk of T2DM, and whether pancreatic fat content is associated with glycaemic control in T2DM.A retrospective survey of a radiology database identified 45 patients with T2DM, and 81 "at risk for T2DM" patients who developed diabetes, between 0.6 and 3.7 years after magnetic resonance imaging (MRI). A control group who did not develop diabetes during a 5-year follow-up and without known metabolic syndrome, liver, or pancreatic diseases were also identified. Fat content was measured by placing regions of interest (ROIs) on in-phase and out-of-phase chemical shift MRI images. Multiple clinical parameters including body mass index, cholesterol levels, blood pressure, glycated haemoglobin (HbA1c; in T2DM group) were collected.There was a significant difference between the T2DM and control groups for fat fraction in the pancreatic head (p=0.043), body (p=0.015), and tail (p=0.001), but not liver (p=0.107). On regression analysis, only the fat fraction within the pancreatic tail was significantly different between control group and "at risk" for T2DM group (p=0.007). A pancreatic tail fat content of >10% had a sensitivity of 45.5% and specificity of 81.3% for predicting development of T2DM within 4 years. Pancreatic fat content was not associated with glycaemic control.Increased fat in the pancreatic tail may identify patients at risk for T2DM.Copyright © 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

To develop a fully automatic algorithm for abdominal organs and adipose tissue compartments segmentation and to assess organ and adipose tissue volume changes in longitudinal water-fat magnetic resonance imaging (MRI) data.Axial two-point Dixon images were acquired in 20 obese women (age range 24-65, BMI 34.9±3.8kg/m(2)) before and after a four-week calorie restriction. Abdominal organs, subcutaneous adipose tissue (SAT) compartments (abdominal, anterior, posterior), SAT regions along the feet-head direction and regional visceral adipose tissue (VAT) were assessed by a fully automatic algorithm using morphological operations and a multi-atlas-based segmentation method.The accuracy of organ segmentation represented by Dice coefficients ranged from 0.672±0.155 for the pancreas to 0.943±0.023 for the liver. Abdominal SAT changes were significantly greater in the posterior than the anterior SAT compartment (-11.4%±5.1% versus -9.5%±6.3%, p<0.001). The loss of VAT that was not located around any organ (-16.1%±8.9%) was significantly greater than the loss of VAT 5cm around liver, left and right kidney, spleen, and pancreas (p<0.05).The presented fully automatic algorithm showed good performance in abdominal adipose tissue and organ segmentation, and allowed the detection of SAT and VAT subcompartments changes during weight loss.Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The goal of this study is to provide a theoretical framework for accurately optimizing the segmentation energy considering all of the possible shapes generated from the level-set-based statistical shape model (SSM). The proposed algorithm solves the well-known open problem, in which a shape prior may not be optimal in terms of an objective functional that needs to be minimized during segmentation. The algorithm allows the selection of an optimal shape prior from among all possible shapes generated from an SSM by conducting a branch-and-bound search over an eigenshape space. The proposed algorithm does not require predefined shape templates or the construction of a hierarchical clustering tree before graph-cut segmentation. It jointly optimizes an objective functional in terms of both the shape prior and segmentation labeling, and finds an optimal solution by considering all possible shapes generated from an SSM. We apply the proposed algorithm to both pancreas and spleen segmentation using multiphase computed tomography volumes, and we compare the results obtained with those produced by a conventional algorithm employing a branch-and-bound search over a search tree of predefined shapes, which were sampled discretely from an SSM. The proposed algorithm significantly improves the segmentation performance in terms of the Jaccard index and Dice similarity index. In addition, we compare the results with the state-of-the-art multiple abdominal organs segmentation algorithm, and confirmed that the performances of both algorithms are comparable to each other. We discuss the high computational efficiency of the proposed algorithm, which was determined experimentally using a normalized number of traversed nodes in a search tree, and the extensibility of the proposed algorithm to other SSMs or energy functionals. Copyright © 2015 Elsevier B.V. All rights reserved.

胰腺因其解剖结构复杂多变、周围环境复杂等特点，始终是医学图像分割中最具挑战性的任务之一．针对以上问题，提出一种融合双解码和全局注意力上采样模块的深度学习分割模型（Combining Dual Decoding and Global Attention Upsampling Modules Network，DGANet）．模型由一个编码器和两个解码器构成，两个解码器实现了对不同深度特征信息的充分利用；模型采用全局注意力上采样模块（Global Attention Upsampling，GAU），利用高层丰富的语义信息来引导低层选择更为精准的特征信息．利用长海医院提供的数据集进行实验，结果表明平均Dice相似系数为86.28%，交并比（Intersection-over-Union，IoU）为0.77，豪斯多夫距离（Hausdorff Distance，HD）为7.7 mm，数据证实了该模型在胰腺囊性肿瘤分割中具有一定的临床意义和价值．

Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.

To compare systematically quantitative MRI, MR spectroscopy (MRS), and different histological methods for liver fat quantification in order to identify possible incongruities.Fifty-nine consecutive patients with liver disorders were examined on a 3 T MRI system. Quantitative MRI was performed using a dual- and a six-echo variant of the modified Dixon (mDixon) sequence, calculating proton density fat fraction (PDFF) maps, in addition to single-voxel MRS. Histological fat quantification included estimation of the percentage of hepatocytes containing fat vesicles as well as semi-automatic quantification (qHisto) using tissue quantification software.In 33 of 59 patients, the hepatic fat fraction was >5% as determined by MRS (maximum 45%, mean 17%). Dual-echo mDixon yielded systematically lower PDFF values than six-echo mDixon (mean difference 1.0%; P < 0.001). Six-echo mDixon correlated excellently with MRS, qHisto, and the estimated percentage of hepatocytes containing fat vesicles (R = 0.984, 0.967, 0.941, respectively, all P < 0.001). Mean values obtained by the estimated percentage of hepatocytes containing fat were higher by a factor of 2.5 in comparison to qHisto. Six-echo mDixon and MRS showed the best agreement with values obtained by qHisto.Six-echo mDixon, MRS, and qHisto provide the most robust and congruent results and are therefore most appropriate for reliable quantification of liver fat.• Six-echo mDixon correlates excellently with MRS, qHisto, and the estimated percentage of fat-containing hepatocytes. • Six-echo mDixon, MRS, and qHisto provide the most robust and congruent results. • Dual-echo mDixon yields systematically lower PDFF values than six-echo mDixon. • The percentage of fat-containing hepatocytes is 2.5-fold higher than fat fraction determined by qHisto. • Performance characteristics and systematic differences of the various methods should be considered.

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.Copyright © 2012 Elsevier Inc. All rights reserved.

A growing body of evidence suggests that intrapancreatic fat is associated with diabetes, but whether distribution of intrapancreatic fat across the regions of the pancreas has a pathophysiologic role is unknown. The aim of this study was to investigate the differences in intrapancreatic fat deposition between the head, body, and tail of the pancreas, as well as the relationship between regional intrapancreatic fat deposition and diabetes status and insulin traits. A total of 368 adults from the general population underwent MRI on a 3 Tesla scanner, and intrapancreatic fat was manually quantified in duplicate. Statistical models included adjustment for age, sex, ethnicity, BMI, and liver fat. Intrapancreatic fat deposition in the head, body, and tail of the pancreas did not differ significantly in adjusted models in either the overall cohort or the three subgroups based on diabetes status. HOMA of insulin resistance and fasting insulin were significantly positively associated with fat in the tail and body of the pancreas. There was no significant association between regional intrapancreatic fat and HOMA of β-cell function. The association of increased intrapancreatic fat deposition in the tail and body regions with increased insulin resistance may have an important role in the early identification of patients at risk for developing insulin resistance and diseases that stem from it.