The primary aim of the website is to support the visualisation of cardiac and aortic imaging phenotypes derived from UK Biobank cardiovascular magnetic resonance (CMR) images for over 26,000 subjects and to enable the exploration of their links to other non imaging phenotypes. These phenotypes characterise the structure and function for the four cardiac chambers and two sections of the aorta, including the left ventricle (LV), right ventricle (RV), left atrium (LA) and right atrium (RA), the ascending aorta (AAo) and descending aorta (DAo).
The CMR imaging data is available from the UK Biobank and acquired following a standardised imaging protocol (Petersen et al. JCMR 2016). The imaging phenotypes are derived using an automated image analysis pipeline, followed by manual quality control. The analysis pipeline consists of several parts, including performing segmentation on short-axis, long-axis and aortic cine images using convolutional neural networks (Bai et al. JCMR 2018, Bai et al. MICCAI 2018), evaluating volumetric measures and myocardial wall thickness, performing motion tracking using image registration, evaluating strains etc. The myocardial wall thickness and strains are evaluated both globally and regionally for each AHA segment.
The visualisation results are organised into three parts: “Phenotypes”, “Phenome-wide associations” and “Correlations”, explained below. You can go to each part by clicking the corresponding tab at the top of this website.
The distribution of each imaging phenotype. It also visualises the sex and age-specific distribution.
A Manhattan plot exploring the uni-variate correlations between each imaging phenotype and each non-imaging phenotypes of the participants. Age, sex, weight and height are regressed out of the imaging phenotypes, as they may confound with many non-imaging phenotypes. The non-imaging phenotypes cover a wide range of participant characteristics, including including primary demographics, early life factors, education and employment, diet summary, alcohol summary, smoking summary, physical activity, physical measure summary, self-reported medical conditions, mental health and cognitive function.
Correlation plots between one imaging phenotype and one non-imaging phenotype. If the non-imaging phenotype is a continuous variable, the kernel density plot and linear regression line are displayed. If it is a categorical variable, the violin plot is displayed.
ACCESSIBILITY | Feedback: Email | Website maintained by Shuo Wang (Last updated: 19 Nov 2019)
By default, a static image is shown. Click the Interactive view button to explore further.
Tips: Click on legends to show/hide anatomy.
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