High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throughput analysis of cardiac imaging data automatically, a pipeline should comprise quality monitoring of the input images, segmentation of the cardiac structures, assessment of the segmentation quality, and parsing of cardiac functional indexes. We present a fully automatic, high throughput image parsing workflow for the analysis of cardiac MR images, and test its performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is capable of performing end-to-end image processing including: data organisation, image quality assessment, shape model initialisation, segmentation, segmentation quality assessment, and functional parameter computation; all without any user interaction. To the best of our knowledge, this is the first paper tackling the fully automatic 3D analysis of the UKB population study, providing reference ranges for all key cardiovascular functional indexes, from both left and right ventricles of the heart. We tested our workflow on a reference cohort of 800 healthy subjects for which manual delineations, and reference functional indexes exist. Our results show statistically significant agreement between the manually obtained reference indexes, and those automatically computed using our framework

MULTI-X, a State-of-the-Art Cloud-Based Ecosystem for Biomedical Research

With the exponential growth of clinical data, and the fast development of AI technologies, researchers are facing unprecedented challenges in managing data storage, scalable processing, and analysis capabilities for heterogeneous multisourced datasets. Beyond the complexity of executing data-intensive workflows over large-scale distributed data, the reproducibility of computed results is of paramount importance to validate scientific discoveries. In this paper, we present MULTIX, a cross-domain research-oriented platform, designed for collaborative and reproducible science. This cloud-based framework simplifies the logistical challenges of implementing data analytics and AI solutions by providing pre-configured environments with ad-hoc scalable computing resources and secure distributed storage, to efficiently build, test, share and reproduce scientific pipelines. An exemplary use-case in the area of cardiac image analysis will be presented together with the practical application of the platform for the analysis of ~20.000 subjects of the UK-Biobank database

3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approach, fully automatic large-scale 3D analysis is essential. In this work, we propose a novel deep neural network using both CMR images and patient metadata to directly predict cardiac shape parameters. The proposed method uses the promising ability of statistical shape models to simplify shape complexity and variability together with the advantages of convolutional neural networks for the extraction of solid visual features. To the best of our knowledge, this is the first work that uses such an approach for 3D cardiac shape prediction. We validated our proposed CMR analytics method against a reference cohort containing 500 3D shapes of the cardiac ventricles. Our results show broadly significant agreement with the reference shapes in terms of the estimated volume of the cardiac ventricles, myocardial mass, 3D Dice, and mean and Hausdorff distance

Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation

Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These studies enable the early discovery of alterations due to impending disease, and enable early identification of individuals at risk. Such studies pose new challenges requiring automatic image analysis. To date, few large-scale population-level cardiac imaging studies have been conducted. One such study stands out for its sheer size, careful implementation, and availability of top quality expert annotation; the UK Biobank (UKB). The resulting massive imaging datasets (targeting ca. 100,000 subjects) has put published approaches for cardiac image quantification to the test. In this paper, we present and evaluate a cardiac magnetic resonance (CMR) image analysis pipeline that properly scales up and can provide a fully automatic analysis of the UKB CMR study. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional bi-ventricular quantification. All this, while maintaining relevant quality controls of the CMR input images, and resulting image segmentations. To the best of our knowledge, this is the first published attempt to fully automate the extraction of global and regional reference ranges of all key functional cardiovascular indexes, from both left and right cardiac ventricles, for a population of 20,000 subjects imaged at 50 time frames per subject, for a total of one million CMR volumes. In addition, our pipeline provides 3D anatomical bi-ventricular models of the heart. These models enable the extraction of detailed information of the morphodynamics of the two ventricles for subsequent association to genetic, omics, lifestyle habits, exposure information, and other information provided in population imaging studies. We validated our proposed CMR analytics pipeline against manual expert readings on a reference cohort of 4620 subjects with contour delineations and corresponding clinical indexes. Our results show broad significant agreement between the manually obtained reference indexes, and those automatically computed via our framework. 80.67% of subjects were processed with mean contour distance of less than 1 pixel, and 17.50% with mean contour distance between 1 and 2 pixels. Finally, we compare our pipeline with a recently published approach reporting on UKB data, and based on deep learning. Our comparison shows similar performance in terms of segmentation accuracy with respect to human experts

Quantitative Analysis of Cardiac Magnetic Resonance in Population Imaging

According to the World Health Organisation, cardiovascular diseases are the most prevalent cause of death worldwide and taking nearly 18 million lives each year. Identifying individuals at risk of cardiovascular diseases and ensuring they receive appropriate treatment in time can prevent premature deaths. Early quantitative assessment of cardiac function, structure, and motion support preventive care and early cardiovascular treatment. Therefore, fully automated analysis and interpretation of large-scale population-based cardiovascular magnetic resonance imaging studies become of high importance. This analysis helps to identify patterns and trends across population groups, and accordingly, reveal insights into key risk factors before diseases fully develop. To date, few large-scale population-level cardiac imaging studies have been conducted. UK Biobank (UKB) is currently the world’s most extensive prospective population study, which in addition to various biological and physical measurements, contain cardiovascular magnetic resonance (CMR) images to establish cardiovascular imaging-derived phenotypes. CMR is an essential element of multi-organ multi-modality imaging visits for patients in multiple dedicated UK Biobank imaging centres that will acquire and store imaging data from 100,000 participants by 2023. This thesis introduces CMR image analysis methods that appropriately scales up and can provide a fully automatic 3D analysis of the UKB CMR studies. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional quantification. Besides, our pipelines provide 3D anatomical models of cardiac structures, which enable the extraction of detailed information of the morphodynamics of the cardiac structures for subsequent associations to genetic, omics, lifestyle habits, exposure information, and other available information in population imaging studies. We present the quantification results from 40,000 subjects of the UK Biobank at 50 time-frames, i.e. two million image volumes

Pathological Cluster Identification by Unsupervised Analysis in 3,822 UK Biobank Cardiac MRIs

International audienceWe perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (MRIs) of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Image Quality Assessment for Population Cardiac MRI: From Detection to Synthesis

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Left Ventricular (LV) cardiac anatomy and function are widely used for diagnosis and monitoring disease progression in cardiology and to assess the patient's response to cardiac surgery and interventional procedures. For population imaging studies, CMR is arguably the most comprehensive imaging modality for non-invasive and non-ionising imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Due to insufficient radiographer's experience in planning a scan, natural cardiac muscle contraction, breathing motion, and imperfect triggering, CMR can display incomplete LV coverage, which hampers quantitative LV characterization and diagnostic accuracy. To tackle this limitation and enhance the accuracy and robustness of the automated cardiac volume and functional assessment, this thesis focuses on the development and application of state-of-the-art deep learning (DL) techniques in cardiac imaging. Specifically, we propose new image feature representation types that are learnt with DL models and aimed at highlighting the CMR image quality cross-dataset. These representations are also intended to estimate the CMR image quality for better interpretation and analysis. Moreover, we investigate how quantitative analysis can benefit when these learnt image representations are used in image synthesis. Specifically, a 3D fisher discriminative representation is introduced to identify CMR image quality in the UK Biobank cardiac data. Additionally, a novel adversarial learning (AL) framework is introduced for the cross-dataset CMR image quality assessment and we show that the common representations learnt by AL can be useful and informative for cross-dataset CMR image analysis. Moreover, we utilize the dataset invariance (DI) representations for CMR volumes interpolation by introducing a novel generative adversarial nets (GANs) based image synthesis framework, which enhance the CMR image quality cross-dataset

Improved outcome prediction in tetralogy of Fallot

Successful advances in cardiac surgery have led to a paradigm shift in the management of an expanding population of repaired tetralogy of Fallot (rTOF) patients. However, late morbidity and mortality have not been abolished, with patients vulnerable to arrhythmia and sudden death. Outcome prediction remains challenging, mandating the identification of novel sensitive and specific non-invasive biomarkers. Cardiac fibrosis in rTOF has been shown to correlate to adverse clinical features, and therefore merits further study, particularly with regards to interstitial fibrosis. Cardiac remodelling following surgical pulmonary valve replacement in patients with rTOF was investigated. Structural reverse remodelling was observed to occur immediately after surgery, followed by gradual biological remodelling. A proactive surgical approach before right ventricular (RV) end-systolic indexed volumes exceed 82ml/m2 confers optimal postoperative RV normalisation. Novel cardiovascular magnetic resonance T1 mapping techniques were developed and tested to improve identification of RV interstitial cardiac fibrosis in rTOF. Multi-echo imaging to separate fat from myocardium, combined with blood signal suppression is promising as a feasible method in saturation-recovery T1 mapping, but requires further technical study prior to clinical application and validation. The genomic signatures of the pathological RV in rTOF were investigated by next generation RNA sequencing. Differential gene expression was evident, and potential molecular determinants of fibrotic and restrictive phenotypes were ascertained. Ubiquitin C may have important functional implications as a ‘network hub’ gene in rTOF. Finally, the longitudinal predictive role of neurohormone expression in patients with rTOF was examined. Neurohormonal activation was confirmed in rTOF, with serum brain natriuretic peptide being prognostic for mortality and sustained arrhythmias during extended follow-up. In conclusion, this work reflects the complex interplay of candidate biomarkers in influencing clinical outcomes. Myocardial fibrosis in rTOF remains a key diagnostic and therapeutic target for improving risk stratification and ameliorating morbidity in the lifelong care of these individuals.Open Acces

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities