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BackgroundLate Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.MethodsThe image database consisted of 60 multicenter, multivendor LGE CMR image datasets from patients with AF, with 30 images taken before and 30 after RFCA for the treatment of AF. A reference standard for scar and fibrosis was established by merging manual segmentations from three observers. Furthermore, scar was also quantified using 2, 3 and 4 standard deviations (SD) and full-width-at-half-maximum (FWHM) methods. Seven institutions responded to the challenge: Imperial College (IC), Mevis Fraunhofer (MV), Sunnybrook Health Sciences (SY), Harvard/Boston University (HB), Yale School of Medicine (YL), King’s College London (KCL) and Utah CARMA (UTA, UTB). There were 8 different algorithms evaluated in this study.ResultsSome algorithms were able to perform significantly better than SD and FWHM methods in both pre- and post-ablation imaging. Segmentation in pre-ablation images was challenging and good correlation with the reference standard was found in post-ablation images. Overlap scores (out of 100) with the reference standard were as follows: Pre: IC = 37, MV = 22, SY = 17, YL = 48, KCL = 30, UTA = 42, UTB = 45; Post: IC = 76, MV = 85, SY = 73, HB = 76, YL = 84, KCL = 78, UTA = 78, UTB = 72.ConclusionsThe study concludes that currently no algorithm is deemed clearly better than others. There is scope for further algorithmic developments in LA fibrosis and scar quantification from LGE CMR images. Benchmarking of future scar segmentation algorithms is thus important. The proposed benchmarking framework is made available as open-source and new participants can evaluate their algorithms via a web-based interface.

The rapid increase in the prevalence of diabetes in middle-aged and elderly people in China is associated with the combination of exposure to famine during the fetal stage and childhood and high economic status in adulthood. Our findings may partly explain the booming diabetes phenomenon in China.

Exposure to famine in early life had sex-specific association with MS. It also suggests the adverse effects of malnutrition might extend beyond the 'first 1000 days' and last 9 years.

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Background and aims: Obesity, especially abdominal obesity, has been considered a risk factor for diabetic complications. Many abdominal obesity indices have been established, including neck circumference (NC), waist-to-hip ratio (WHR), lipid accumulation product (LAP), visceral adiposity index (VAI) and the Chinese visceral adiposity index (CVAI). However, studies investigating the associations between these indices and diabetic complications are limited. The objective of this study was to investigate the associations of the abdominal obesity indices with cardiovascular and cerebrovascular disease (CVD), diabetic kidney disease (DKD) and diabetic retinopathy (DR). Methods: A total of 4658 diabetic participants were enrolled from seven communities in Shanghai, China, in 2018. Participants completed questionnaires and underwent blood pressure, glucose, lipid profile, and urine albumin/creatinine ratio measurements; fundus photographs; and anthropometric parameters, including height, weight, waist circumference (WC), NC and hip circumference (HC). Results: In men, a one standard deviation (SD) increase in CVAI level was significantly associated with a greater prevalence of CVD (OR 1.35; 95% CI 1.13, 1.62) and DKD (OR 1.38; 95% CI 1.12, 1.70) (both P < 0.05). In women, a one SD increase in CVAI level was significantly associated with a greater prevalence of CVD (OR 1.32; 95% CI 1.04, 1.69) and DKD (OR 2.50; 95% CI 1.81, 3.47) (both P < 0.05). A one SD increase in NC was significantly associated with a greater prevalence of CCA plaque in both men (OR 1.26; 95% CI 1.10, 1.44) and women (OR 1.20; 95% CI 1.07, 1.35). These associations were all adjusted for potential confounding factors. Conclusions: CVAI was most strongly associated with the prevalence of CVD and DKD among the abdominal obesity indices, and NC was unique associated with the prevalence of CCA plaque in Chinese adults with diabetes.

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The motivation of the segmentation challenge is to quantitatively analyze global and regional cardiac function from cine magnetic resonance (MR) images, clinical parameters such as ejection fraction (EF), left ventricle myocardium mass (MM), and stroke volume (SV) are required. Calculations of these parameters depend upon accurate delineation of endocardial and epicardial contours of the left ventricle (LV). Manual delineation is time-consuming and tedious and has high inter-observer variability. Thus, fully automatic LV segmentation is desirable.The automatic segmentation of the LV in cine MR typically faces four challenges: 1) the overlap between the intensity distributions within the cardiac regions; 2) the lack of edge information; 3) the shape variability of the endocardial and epicardial contours across slices and phases; and 4) the inter-subject variability of these factors. A number of methods have been proposed for (semi-) automatic LV segmentation, including using a probability atlas [1], dynamic programming [2-3], fuzzy clustering [4], a deformable model [5], an active appearance model [6], a variational and level set [7-10], graph cuts [11-12] and an image-driven approach [13]. For a complete review of recent literature describing cardiac segmentation techniques, see [14]. Although the segmentation results have improved, accurate LV segmentation is still acknowledged as a difficult problem.The goals of this contest are to compare LV segmentation methods by providing an evaluation system, and a database of images and expert contours. Comparing segmentation results across research studies can be difficult due to unspecified differences in the method or implementation of evaluation metrics. This contest will provide open-source code for contour evaluation. Furthermore, the database will provide a set of images such that confounding segmentation differences due to image quality or pathology could be eliminated.