Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

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  • Journal article
    Biffi C, Cerrolaza Martinez JJ, Tarroni G, Bai W, Simoes Monteiro de Marvao A, Oktay O, Ledig C, Le Folgoc L, Kamnitsas K, Doumou G, Duan J, Prasad S, Cook S, O'Regan D, Rueckert Det al., 2020,

    Explainable anatomical shape analysis through deep hierarchical generative models

    , IEEE Transactions on Medical Imaging, Vol: 39, Pages: 2088-2099, ISSN: 0278-0062

    Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer’s disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.

  • Journal article
    Chen C, Bai W, Davies R, Bhuva A, Manisty C, Moon J, Aung N, Lee A, Sanghvi M, Fung K, Paiva J, Petersen S, Lukaschuk E, Piechnik S, Neubauer S, Rueckert Det al.,

    Improving the generalizability of convolutional neural network-based segmentation on CMR images

    , Frontiers in Cardiovascular Medicine, ISSN: 2297-055X
  • Journal article
    Martínez V, Fernando S, Molina-Solana M, Guo Yet al., 2020,

    Tuoris: A middleware for visualizing dynamic graphics in scalable resolution display environments

    , Future Generation Computer Systems, Vol: 106, Pages: 559-571, ISSN: 0167-739X

    In the era of big data, large-scale information visualization has become an important challenge. Scalable resolution display environments (SRDEs) have emerged as a technological solution for building high-resolution display systems by tiling lower resolution screens. These systems bring serious advantages, including lower construction cost and better maintainability compared to other alternatives. However, they require specialized software but also purpose-built content to suit the inherently complex underlying systems. This creates several challenges when designing visualizations for big data, such that can be reused across several SRDEs of varying dimensions. This is not yet a common practice but is becoming increasingly popular among those who engage in collaborative visual analytics in data observatories. In this paper, we define three key requirements for systems suitable for such environments, point out limitations of existing frameworks, and introduce Tuoris, a novel open-source middleware for visualizing dynamic graphics in SRDEs. Tuoris manages the complexity of distributing and synchronizing the information among different components of the system, eliminating the need for purpose-built content. This makes it possible for users to seamlessly port existing graphical content developed using standard web technologies, and simplifies the process of developing advanced, dynamic and interactive web applications for large-scale information visualization. Tuoris is designed to work with Scalable Vector Graphics (SVG), reducing bandwidth consumption and achieving high frame rates in visualizations with dynamic animations. It scales independent of the display wall resolution and contrasts with other frameworks that transmit visual information as blocks of images.

  • Journal article
    Ali MK, Kim RY, Brown AC, Mayall JR, Karim R, Pinkerton JW, Liu G, Martin KL, Starkey MR, Pillar A, Donovan C, Pathinayake PS, Carroll OR, Trinder D, Tay HL, Badi YE, Kermani NZ, Guo Y-K, Aryal R, Mumby S, Pavlidis S, Adcock IM, Weaver J, Xenaki D, Oliver BG, Holliday EG, Foster PS, Wark PA, Johnstone DM, Milward EA, Hansbro PM, Horvat JCet al., 2020,

    Crucial role for lung iron level and regulation in the pathogenesis and severity of asthma.

    , European Respiratory Journal, Vol: 55, Pages: 1-14, ISSN: 0903-1936

    Accumulating evidence highlights links between iron regulation and respiratory disease. Here, we assessed the relationship between iron levels and regulatory responses in clinical and experimental asthma.We show that cell-free iron levels are reduced in the bronchoalveolar lavage (BAL) supernatant of severe or mild-moderate asthma patients and correlate with lower forced expiratory volume in 1 s (FEV1). Conversely, iron-loaded cell numbers were increased in BAL in these patients and with lower FEV1/forced vital capacity (FEV1/FVC). The airway tissue expression of the iron sequestration molecules divalent metal transporter 1 (DMT1) and transferrin receptor 1 (TFR1) are increased in asthma with TFR1 expression correlating with reduced lung function and increased type 2 (T2) inflammatory responses in the airways. Furthermore, pulmonary iron levels are increased in a house dust mite (HDM)-induced model of experimental asthma in association with augmented Tfr1 expression in airway tissue, similar to human disease. We show that macrophages are the predominant source of increased Tfr1 and Tfr1+ macrophages have increased Il13 expression. We also show that increased iron levels induce increased pro-inflammatory cytokine and/or extracellular matrix (ECM) responses in human airway smooth muscle (ASM) cells and fibroblasts ex vivo and induce key features of asthma, including airway hyper-responsiveness and fibrosis and T2 inflammatory responses, in vivoTogether these complementary clinical and experimental data highlight the importance of altered pulmonary iron levels and regulation in asthma, and the need for a greater focus on the role and potential therapeutic targeting of iron in the pathogenesis and severity of disease.

  • Journal article
    Bhuva AN, Treibel TA, De Marvao A, Biffi C, Dawes TJW, Doumou G, Bai W, Patel K, Boubertakh R, Rueckert D, O'Regan DP, Hughes AD, Moon JC, Manisty CHet al., 2020,

    Sex and regional differences inmyocardial plasticity in aortic stenosis are revealed by 3D modelmachine learning

    , EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 21, Pages: 417-427, ISSN: 2047-2404
  • Journal article
    Jolliffe DA, Stefanidis C, Wang Z, Kermani NZ, Dimitrov V, White JH, McDonough JE, Janssens W, Pfeffer P, Griffiths CJ, Bush A, Guo Y, Christenson S, Adcock IM, Chung KF, Thummel KE, Martineau ARet al., 2020,

    Vitamin D metabolism is dysregulated in asthma and chronic obstructive pulmonary disease.

    , American Journal of Respiratory and Critical Care Medicine, Vol: 202, Pages: 371-382, ISSN: 1073-449X

    RATIONALE: Vitamin D deficiency is common in patients with asthma and COPD. Low 25-hydroxyvitamin D (25[OH]D) levels may represent a cause or a consequence of these conditions. OBJECTIVE: To determine whether vitamin D metabolism is altered in asthma or COPD. METHODS: We conducted a longitudinal study in 186 adults to determine whether the 25(OH)D response to six oral doses of 3 mg vitamin D3, administered over one year, differed between those with asthma or COPD vs. controls. Serum concentrations of vitamin D3, 25(OH)D3 and 1α,25-dihydroxyvitamin D3 (1α,25[OH]2D3) were determined pre- and post-supplementation in 93 adults with asthma, COPD or neither condition, and metabolite-to-parent compound molar ratios were compared between groups to estimate hydroxylase activity. Additionally, we analyzed fourteen datasets to compare expression of 1α,25[OH]2D3-inducible gene expression signatures in clinical samples taken from adults with asthma or COPD vs. controls. MEASUREMENTS AND MAIN RESULTS: The mean post-supplementation 25(OH)D increase in participants with asthma (20.9 nmol/L) and COPD (21.5 nmol/L) was lower than in controls (39.8 nmol/L; P=0.001). Compared with controls, patients with asthma and COPD had lower molar ratios of 25(OH)D3-to-vitamin D3 and higher molar ratios of 1α,25(OH)2D3-to-25(OH)D3 both pre- and post-supplementation (P≤0.005). Inter-group differences in 1α,25[OH]2D3-inducible gene expression signatures were modest and variable where statistically significant. CONCLUSIONS: Attenuation of the 25(OH)D response to vitamin D supplementation in asthma and COPD associated with reduced molar ratios of 25(OH)D3-to-vitamin D3 and increased molar ratios of 1α,25(OH)2D3-to-25(OH)D3 in serum, suggesting that vitamin D metabolism is dysregulated in these conditions.

  • Journal article
    Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, Rueckert Det al., 2020,

    Deep learning for cardiac image segmentation: A review

    , Frontiers in Cardiovascular Medicine, Vol: 7, Pages: 1-33, ISSN: 2297-055X

    Deep learning has become the most widely used approach for cardiac imagesegmentation in recent years. In this paper, we provide a review of over 100cardiac image segmentation papers using deep learning, which covers commonimaging modalities including magnetic resonance imaging (MRI), computedtomography (CT), and ultrasound (US) and major anatomical structures ofinterest (ventricles, atria and vessels). In addition, a summary of publiclyavailable cardiac image datasets and code repositories are included to providea base for encouraging reproducible research. Finally, we discuss thechallenges and limitations with current deep learning-based approaches(scarcity of labels, model generalizability across different domains,interpretability) and suggest potential directions for future research.

  • Journal article
    Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King APet al., 2020,

    Fully automated, quality-controlled cardiac analysis from CMR: Validation and large-scale application to characterize cardiac function

    , JACC: Cardiovascular Imaging, Vol: 13, Pages: 684-695, ISSN: 1876-7591

    OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output. BACKGROUND: Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. METHODS: The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps' ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. RESULTS: Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. CONCLUSIONS: The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.

  • Journal article
    Dewey M, Siebes M, Kachelrieß M, Kofoed KF, Maurovich-Horvat P, Nikolaou K, Bai W, Kofler A, Manka R, Kozerke S, Chiribiri A, Schaeffter T, Michallek F, Bengel F, Nekolla S, Knaapen P, Lubberink M, Senior R, Tang M-X, Piek JJ, van de Hoef T, Martens J, Schreiber L, Quantitative Cardiac Imaging Study Groupet al., 2020,

    Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia

    , Nature Reviews Cardiology, Vol: 17, Pages: 427-450, ISSN: 1759-5002

    Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality.

  • Journal article
    Tarroni G, Bai W, Oktay O, Schuh A, Suzuki H, Glocker B, Matthews P, Rueckert Det al., 2020,

    Large-scale quality control of cardiac imaging in population studies: application to UK Biobank

    , Scientific Reports, Vol: 10, ISSN: 2045-2322

    In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment isunfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) imagesto the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics(heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factorsincluding acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage(i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of thestacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slicemotion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastoliccardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involvedin UKBB CMR acquisition and for the ones who use the dataset for research purposes.

  • Conference paper
    Chen C, Ouyang C, Tarroni G, Schlemper J, Qiu H, Bai W, Rueckert Det al., 2020,

    Unsupervised multi-modal style transfer for cardiac MR segmentation

    , MICCAI STACOM Workshop, Publisher: Springer International Publishing, Pages: 209-219, ISSN: 0302-9743

    In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.

  • Conference paper
    Lu P, Qiu H, Qin C, Bai W, Rueckert D, Noble JAet al., 2020,

    Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features

    , Pages: 294-306, ISSN: 1865-0929

    This paper shows that deep modelling of subtle changes of cardiac motion can help in automated diagnosis of early onset of cardiac disease. In this paper, we model left ventricular (LV) cardiac motion in MRI sequences, based on a hybrid spatio-temporal network. Temporal data over long time periods is used as inputs to the model and delivers a dense displacement field (DDF) for regional analysis of LV function. A segmentation mask of the end-diastole (ED) frame is deformed by the predicted DDF from which regional analysis of LV function endocardial radius, thickness, circumferential strain (Ecc) and radial strain (Err) are estimated. Cardiac motion is estimated over MR cine loops. We compare the proposed technique to two other deep learning-based approaches and show that the proposed approach achieves promising predicted DDFs. Predicted DDFs are estimated on imaging data from healthy volunteers and patients with primary pulmonary hypertension from the UK Biobank. Experiments demonstrate that the proposed methods perform well in obtaining estimates of endocardial radii as cardiac motion-characteristic features for regional LV analysis.

  • Conference paper
    Wang S, Tarroni G, Qin C, Mo Y, Dai C, Chen C, Glocker B, Guo Y, Rueckert D, Bai Wet al., 2020,

    Deep Generative Model-Based Quality Control for Cardiac MRI Segmentation.

    , Publisher: Springer, Pages: 88-97
  • Conference paper
    Truong N, Sun K, Guo Y, 2019,

    Blockchain-based personal data management: from fiction to solution

    , The 18th IEEE International Symposium on Network Computing and Applications (NCA 2019), Publisher: IEEE, Pages: 1-8

    The emerging blockchain technology has enabledvarious decentralised applications in a trustless environmentwithout relying on a trusted intermediary. It is expected as apromising solution to tackle sophisticated challenges on personaldata management, thanks to its advanced features such as im-mutability, decentralisation and transparency. Although certainapproaches have been proposed to address technical difficultiesin personal data management; most of them only provided pre-liminary methodological exploration. Alarmingly, when utilisingBlockchain for developing a personal data management system,fictions have occurred in existing approaches and been promul-gated in the literature. Such fictions are theoretically doable;however, by thoroughly breaking down consensus protocols andtransaction validation processes, we clarify that such existingapproaches are either impractical or highly inefficient due tothe natural limitations of the blockchain and Smart Contractstechnologies. This encourages us to propose a feasible solution inwhich such fictions are reduced by designing a novel systemarchitecture with a blockchain-based “proof of permission”protocol. We demonstrate the feasibility and efficiency of theproposed models by implementing a clinical data sharing servicebuilt on top of a public blockchain platform. We believe thatour research resolves existing ambiguity and take a step furtheron providing a practically feasible solution for decentralisedpersonal data management.

  • Conference paper
    Karri SSK, Bach F, Pock T, 2019,

    Fast decomposable submodular function minimization using constrained total variation

    , Neural Information Processing Systems, 2019, Publisher: Neural Information Processing Systems Foundation, Inc.

    We consider the problem of minimizing the sum of submodular set functions as-suming minimization oracles of each summand function. Most existing approachesreformulate the problem as the convex minimization of the sum of the correspond-ing Lovász extensions and the squared Euclidean norm, leading to algorithmsrequiring total variation oracles of the summand functions; without further assump-tions, these more complex oracles require many calls to the simpler minimizationoracles often available in practice. In this paper, we consider a modified convexproblem requiring a constrained version of the total variation oracles that can besolved with significantly fewer calls to the simple minimization oracles. We supportour claims by showing results on graph cuts for 2D and 3D graphs.

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