Yingli Lu

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.

Abstract 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. Trial registration ChiCTR1800017573, www.chictr.org.cn . Registered 04 August 2018.

AIMS: The aim of this study was to test whether current and past night shift work was associated with incident atrial fibrillation (AF) and whether this association was modified by genetic vulnerability. Its associations with coronary heart disease (CHD), stroke, and heart failure (HF) were measured as a secondary aim. METHODS AND RESULTS: This cohort study included 283 657 participants in paid employment or self-employed without AF and 276 009 participants free of CHD, stroke, and HF at baseline in the UK Biobank. Current and lifetime night shift work information was obtained. Cox proportional hazard models were used. Weighted genetic risk score for AF was calculated. During a median follow-up of 10.4 years, 5777 incident AF cases were documented. From 'day workers', 'shift but never/rarely night shifts', and 'some night shifts' to 'usual/permanent night shifts', there was a significant increasing trend in the risk of incident AF (P for trend 0.013). Usual or permanent night shifts were associated with the highest risk [hazard ratio (HR) 1.16, 95% confidence interval (CI) 1.02-1.32]. Considering a person's lifetime work schedule and compared with shift workers never working nights, participants with a duration over 10 years and an average 3-8 nights/month frequency of night shift work exposure possessed higher AF risk (HR 1.18, 95% CI 0.99-1.40 and HR 1.22, 95% CI 1.02-1.45, respectively). These associations between current and lifetime night shifts and AF were not modified by genetic predisposition to AF. Usual/permanent current night shifts, ≥10 years and 3-8 nights/month of lifetime night shifts were significantly associated with a higher risk of incident CHD (HR 1.22, 95% CI 1.11-1.35, HR 1.37, 95% CI 1.20-1.58 and HR 1.35, 95% CI 1.18-1.55, respectively). These associations in stroke and HF were not significant. CONCLUSION: Both current and lifetime night shift exposures were associated with increased AF risk, regardless of genetic AF risk. Night shift exposure also increased the risk of CHD but not stroke or HF. Whether decreasing night shift work frequency and duration might represent another avenue to improve heart health during working life and beyond warrants further study.

BACKGROUND: Late 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. METHODS: The 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. RESULTS: Some 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. CONCLUSIONS: The 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.