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UK Biobank: Automated aortic lumen segmentation results
Aortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in largescale population-based studies. Using 5,100 scans from UK Biobank, this study sought to develop and validate a fully automated method to
1) detect and locate the ROIs of AA and PDA, and
2) provide a quality control mechanism.
The proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.
Please note that this return contains data for the first tranche of results returned by application 2964. Further data are available from return 1971
Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound
Imaging of the heart and blood vessels is performed in a large subset of the UK Biobank cohort. Many measures defining the state of the heart and blood vessels can be derived from the images acquired. These measures are influenced by various health conditions and modifiable and non-modifiable factors, such as age, gender and ethnicity. The aim of this proposal is to describe the measures of the heart and blood vessel in the UK Biobank population and investigate how much modifiable and non-modifiable factors influence them. All new data will be made available for future research. Knowing the reference ranges for common imaging measures of the heart and circulation and how they are influenced by factors, such as age, gender, ethnicity, risk factors for heart attacks and strokes, is key for improving making diagnoses and predicting health outcomes. Descriptive statistics will be performed for all image derived phenotypes (IDPs) from the cardiovascular magnetic resonance (CMR) and carotid ultrasound images. We will perform subgroup analysis for important clinical factors, such as age, gender, cardiovascular risk, chronic conditions (e.g. Diabetes). We will apply descriptive statistics to a subpopulation considered `healthy without cardiovascular disease or presence of modifiable risk factors`. Univariate and multivariate regression analysis will be used to assess relationships between IDPs and relevant co-variates. We will also assess intra- and inter-observer variability for IDP measurement when repeat analysis is available. Initial 5000 subjects from the imaging enhancement study.
|Lead investigator:||Professor Steffen Petersen|
|Lead institution:||Queen Mary University of London|