Imperial College London

DrWenjiaBai

Faculty of MedicineDepartment of Brain Sciences

Lecturer in Artificial Intelligence in Medicine
 
 
 
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Contact

 

+44 (0)20 7594 8291w.bai Website

 
 
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Location

 

Room 212, Data Science InstituteWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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143 results found

Thanaj M, Mielke J, McGurk K, Bai W, Savioli N, Simoes Monteiro de Marvao A, Meyer H, Zeng L, Sohler F, Lumbers T, Wilkins M, Ware J, Bender C, Rueckert D, MacNamara A, Freitag D, O'Regan Det al., 2022, Genetic and environmental determinants of diastolic heart function, Nature Cardiovascular Research, Vol: 1, Pages: 361-371, ISSN: 2731-0590

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends onmyocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processesand is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiacmotion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wideassociation study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomericfunction under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes wereindependent predictors of diastolic function and we found a causal relationship between genetically-determined ventricularstiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolicfunction that are relevant for identifying causal relationships and potential tractable targets.

Journal article

Zhang D, Barbot A, Seichepine F, Lo FP-W, Bai W, Yang G-Z, Lo Bet al., 2022, Micro-object pose estimation with sim-to-real transfer learning using small dataset, COMMUNICATIONS PHYSICS, Vol: 5, ISSN: 2399-3650

Journal article

Davies RH, Augusto JB, Bhuva A, Xue H, Treibel TA, Ye Y, Hughes RK, Bai W, Lau C, Shiwani H, Fontana M, Kozor R, Herrey A, Lopes LR, Maestrini V, Rosmini S, Petersen SE, Kellman P, Rueckert D, Greenwood JP, Captur G, Manisty C, Schelbert E, Moon JCet al., 2022, Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 24, ISSN: 1097-6647

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.

Journal article

Meng Q, Bai W, Liu T, Simoes Monteiro de Marvao A, O'Regan D, Rueckert Det al., 2022, Multiview Motion Estimation for 3D cardiac motion tracking

Code for paper ''MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI''

Software

Dai C, Wang S, Mo Y, Angelini E, Guo Y, Bai Wet al., 2022, Suggestive annotation of brain MR images with gradient-guided sampling, Medical Image Analysis, Vol: 77, Pages: 102373-102373, ISSN: 1361-8415

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.

Journal article

Venkataraman AV, Bai W, Whittington A, Myers JF, Rabiner EA, Lingford-Hughes A, Matthews PMet al., 2021, Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support, Alzheimer's Research and Therapy, Vol: 13, Pages: 1-12, ISSN: 1758-9193

BackgroundAmyloid-β (Aβ) PET has emerged as clinically useful for more accurate diagnosis of patients with cognitive decline. Aβ deposition is a necessary cause or response to the cellular pathology of Alzheimer’s disease (AD). Usual clinical and research interpretation of amyloid PET does not fully utilise all information regarding the spatial distribution of signal. We present a data-driven, spatially informed classifier to boost the diagnostic power of amyloid PET in AD.MethodsVoxel-wise k-means clustering of amyloid-positive voxels was performed; clusters were mapped to brain anatomy and tested for their associations by diagnostic category and disease severity with 758 amyloid PET scans from volunteers in the AD continuum from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A machine learning approach based on this spatially constrained model using an optimised quadratic support vector machine was developed for automatic classification of scans for AD vs non-AD pathology.ResultsThis classifier boosted the accuracy of classification of AD scans to 81% using the amyloid PET alone with an area under the curve (AUC) of 0.91 compared to other spatial methods. This increased sensitivity to detect AD by 15% and the AUC by 9% compared to the use of a composite region of interest SUVr.ConclusionsThe diagnostic classification accuracy of amyloid PET was improved using an automated data-driven spatial classifier. Our classifier highlights the importance of considering the spatial variation in Aβ PET signal for optimal interpretation of scans. The algorithm now is available to be evaluated prospectively as a tool for automated clinical decision support in research settings.

Journal article

De Marvao A, McGurk K, Zheng S, Thanaj M, Bai W, Duan J, Halliday B, Pantazis A, Prasad S, Rueckert D, Walsh R, Ho C, Cook S, Ware J, O'Regan Det al., 2021, Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes in over 200,000 adults, ESC Congress 2021, Publisher: European Society of Cardiology, Pages: 1731-1731, ISSN: 0195-668X

BackgroundHypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.PurposeTo determine the population prevalence of HCM-associated sarcomeric variants, characterise their phenotypic manifestations, estimate penetrance, and identify associations between sarcomeric variants and clinical outcomes, we performed an observational study of 218,813 adults in the UK Biobank (UKBB), of whom 200,584 have whole exome sequencing (WES).MethodsWe carried out an integrated analysis of WES and cardiac magnetic resonance (CMR) imaging in UK Biobank participants stratified by sarcomere-encoding variant status. Computer vision techniques were used to automatically segment the four chambers of the heart (Figure 1). Cardiac motion analysis was used to derive strain and strain rates. Regional analysis of left ventricular wall thickness was performed using three-dimensional modelling of these segmentations.ResultsMedian age at recruitment was 58 (IQR 50–63 years), and participants were followed up for a median of 10.8 years (IQR 9.9–11.6 years) with a total of 19,507 primary clinical events reported.The prevalence of rare variants (allele frequency <0.ehab724.17314) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,727; 1 in 35), and the prevalence of pathogenic or likely pathogenic variants (SARC-P/LP) was 0.24% (n=474, 1 in 423).SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events (MACE) compared to controls (HR 1.68, 95% CI 1.37–2.06, p<0.001), mainly due to heart failure endpoints (Figure 2: cumulative hazard curves with zoomed plots for lifetime risk of A) death and MACE or B) heart failure, stratified by genotype; genotype negative (SARC-NEG), carriers of indeterminate sarcomeric variants (SARC-IND) or SARC-P/LP; C) Forest plot of comparative lifetime risk of c

Conference paper

Chen C, Hammernik K, Ouyang C, Qin C, Bai W, Rueckert Det al., 2021, Cooperative training and latent space data augmentation for robust medical image segmentation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

Conference paper

Wang S, Qin C, Savioli N, Chen C, O'Regan D, Cook S, Guo Y, Rueckert D, Bai Wet al., 2021, Joint motion correction and super resolution for cardiac segmentationvia latent optimisation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration andrespiratory/cardiac motion, stacks of multi-slice 2D images are acquired inclinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility.

Conference paper

Simoes Monteiro de Marvao A, McGurk K, Zheng S, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes T, Savioli N, Halliday B, Xu X, Buchan R, Baksi A, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis A, Zhang X, Jang M, Berry A, Pantazis A, Barton P, Rueckert D, Prasad S, Walsh R, Ho C, Cook S, Ware J, O'Regan Det al., 2021, Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy, Journal of the American College of Cardiology, Vol: 78, Pages: 1097-1110, ISSN: 0735-1097

Background: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomereencoding genes, but little is known about the clinical significance of these variants in thegeneral population.Objectives: To compare lifetime outcomes and cardiovascular phenotypes according to thepresence of rare variants in sarcomere-encoding genes amongst middle-aged adults.Methods: We analysed whole exome sequencing and cardiac magnetic resonance (CMR)imaging in UK Biobank participants stratified by sarcomere-encoding variant status.Results: The prevalence of rare variants (allele frequency <0.00004) in HCM-associatedsarcomere-encoding genes in 200,584 participants was 2.9% (n=5,712; 1 in 35), and theprevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was0.25% (n=493, 1 in 407). SARC-HCM-P/LP variants were associated with increased risk ofdeath or major adverse cardiac events compared to controls (HR 1.69, 95% CI 1.38 to 2.07,p<0.001), mainly due to heart failure endpoints (HR 4.23, 95% CI 3.07 to 5.83, p<0.001). In21,322 participants with CMR, SARC-HCM-P/LP were associated with asymmetric increasein left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p<0.001) buthypertrophy (≥13mm) was only present in 18.4% (n=9/49, 95% CI 9 to 32%). SARC-HCMP/LP were still associated with heart failure after adjustment for wall thickness (HR 6.74,95% CI 2.43 to 18.7, p<0.001).Conclusions: In this population of middle-aged adults, SARC-HCM-P/LP variants have lowaggregate penetrance for overt HCM but are associated with increased risk of adversecardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absoluteevent rates are low, identification of these variants may enhance risk stratification beyondfamilial disease.

Journal article

Thanaj M, Mielke J, McGurk KA, Bai W, Savioli N, de Marvao A, Meyer HV, Zeng L, Sohler F, Wilkins MR, Ware JS, Bender C, Rueckert D, MacNamara A, Freitag DF, ORegan DPet al., 2021, Genetic and environmental determinants of diastolic heart function, Publisher: Cold Spring Harbor Laboratory

<jats:title>ABSTRACT</jats:title><jats:p>Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between ventricular stiffness and heart failure. Our results provide novel insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and tractable targets in heart failure.</jats:p>

Working paper

Evangelou E, Suzuki H, Bai W, Pazoki R, Gao H, Matthews P, Elliott Pet al., 2021, Alcohol consumption in the general population is associated with structural changes in multiple organ systems., eLife, Vol: 10, Pages: 1-15, ISSN: 2050-084X

Background:Excessive alcohol consumption is associated with damage to various organs, but its multi-organ effects have not been characterised across the usual range of alcohol drinking in a large general population sample.Methods:We assessed global effect sizes of alcohol consumption on quantitative magnetic resonance imaging phenotypic measures of the brain, heart, aorta, and liver of UK Biobank participants who reported drinking alcohol.Results:We found a monotonic association of higher alcohol consumption with lower normalised brain volume across the range of alcohol intakes (–1.7 × 10−3 ± 0.76 × 10−3 per doubling of alcohol consumption, p=3.0 × 10−14). Alcohol consumption was also associated directly with measures of left ventricular mass index and left ventricular and atrial volume indices. Liver fat increased by a mean of 0.15% per doubling of alcohol consumption.Conclusions:Our results imply that there is not a ‘safe threshold’ below which there are no toxic effects of alcohol. Current public health guidelines concerning alcohol consumption may need to be revisited.

Journal article

Balaban G, Halliday B, Bradley P, Bai W, Nygaard S, Owen R, Hatipoglu S, Ferreira ND, Izgi C, Tayal U, Corden B, Ware J, Pennell D, Rueckert D, Plank G, Rinaldi CA, Prasad SK, Bishop Met al., 2021, Late-gadolinium enhancement interface area and electrophysiological simulations predict arrhythmic events in non-ischemic dilated cardiomyopathy patients, JACC: Clinical Electrophysiology, Vol: 7, Pages: 238-249, ISSN: 2405-5018

BACKGROUND: The presence of late-gadolinium enhancement (LGE) predicts life threatening ventricular arrhythmias in non-ischemic dilated cardiomyopathy (NIDCM); however, risk stratification remains imprecise. LGE shape and simulations of electrical activity may be able to provide additional prognostic information.OBJECTIVE: This study sought to investigate whether shape-based LGE metrics and simulations of reentrant electrical activity are associated with arrhythmic events in NIDCM patients.METHODS: CMR-LGE shape metrics were computed for a cohort of 156 NIDCM patients with visible LGE and tested retrospectively for an association with an arrhythmic composite end-point of sudden cardiac death and ventricular tachycardia. Computational models were created from images and used in conjunction with simulated stimulation protocols to assess the potential for reentry induction in each patient’s scar morphology. A mechanistic analysis of the simulations was carried out to explain the associations. RESULTS: During a median follow-up of 1611 [IQR 881-2341] days, 16 patients (10.3%) met the primary endpoint. In an inverse probability weighted Cox regression, the LGE-myocardial interface area (HR:1.75; 95% CI:1.24-2.47; p=0.001), number of simulated reentries (HR: 1.4; 95% CI: 1.23-1.59; p<0.01) and LGE volume (HR:1.44; 95% CI:1.07-1.94; p=0.02) were associated with arrhythmic events. Computational modeling revealed repolarisation heterogeneity and rate-dependent block of electrical wavefronts at the LGE-myocardial interface as putative arrhythmogenic mechanisms directly related to LGE interface area.CONCLUSION: The area of interface between scar and surviving myocardium, as well as simulated reentrant activity, are associated with an elevated risk of major arrhythmic events in NIDCM patients with LGE and represent novel risk predictors.

Journal article

de Marvao A, McGurk KA, Zheng SL, Thanaj M, Bai W, Duan J, Biffi C, Mazzarotto F, Statton B, Dawes TJW, Savioli N, Halliday BP, Xu X, Buchan RJ, Baksi AJ, Quinlan M, Tokarczuk P, Tayal U, Francis C, Whiffin N, Theotokis PI, Zhang X, Jang M, Berry A, Pantazis A, Barton PJR, Rueckert D, Prasad SK, Walsh R, Ho CY, Cook SA, Ware JS, ORegan DPet al., 2021, Outcomes and phenotypic expression of rare variants in hypertrophic cardiomyopathy genes amongst UK Biobank participants, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We compared outcomes and cardiovascular phenotypes in UK Biobank participants with whole exome sequencing stratified by sarcomere-encoding variant status.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The prevalence of rare variants (allele frequency &lt;0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n=5,727; 1 in 35), of which 0.24% (n=474, 1 in 423) were pathogenic or likely pathogenic variants (SARC-P/LP). SARC-P/LP variants were associated with increased risk of death or major adverse cardiac events compared to controls (HR 1.68, 95% CI 1.37-2.06, p&lt;0.001), mainly due to heart failure (HR 4.40, 95% CI 3.22-6.02, p&lt;0.001) and arrhythmia (HR 1.55, 95% CI 1.18-2.03, p=0.002). In 21,322 participants with cardiac magnetic resonance imaging, SARC-P/LP were associated with increased left ventricular maximum wall thickness (10.9±2.7 vs 9.4±1.6 mm, p&lt;0.001) and concentric remodelling (mass/volume ratio: 0.63±0.12 vs 0.58±0.09 g/mL, p&lt;0.001), but hypertrophy (≥13mm) was only present in 16% (n=7/43, 95% CI 7-31%). Other rare sarcomere-encoding variants had a weak effect on wall thickness (9.5±1.7 vs 9.4±1.6 mm, p=0.002) with no combined excess cardiovascular risk (HR 1.00 95% CI 0.92-1.08, p=0.9).</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>In the general population, SARC-P/LP variants have low aggregate penetrance for overt HCM bu

Working paper

Tadros R, Francis C, Xu X, Vermeer AMC, Harper AR, Huurman R, Bisabu KK, Walsh R, Hoorntje ET, te Rijdt WP, Buchan RJ, van Velzen HG, van Slegtenhorst MA, Vermeulen JM, Offerhaus JA, Bai W, de Marvao A, Lahrouchi N, Beekman L, Karper JC, Veldink JH, Kayvanpour E, Pantazis A, Baksi AJ, Whiffin N, Mazzarotto F, Sloane G, Suzuki H, Schneider-Luftman D, Elliott P, Richard P, Ader F, Villard E, Lichtner P, Meitinger T, Tanck MWT, van Tintelen JP, Thain A, McCarty D, Hegele RA, Roberts JD, Amyot J, Dube M-P, Cadrin-Tourigny J, Giraldeau G, L'Allier PL, Garceau P, Tardif J-C, Boekholdt SM, Lumbers RT, Asselbergs FW, Barton PJR, Cook SA, Prasad SK, O'Regan DP, van der Velden J, Verweij KJH, Talajic M, Lettre G, Pinto YM, Meder B, Charron P, de Boer RA, Christiaans I, Michels M, Wilde AAM, Watkins H, Matthews PM, Ware JS, Bezzina CRet al., 2021, Shared genetic pathways contribute to risk of hypertrophic and dilated cardiomyopathies with opposite directions of effect, NATURE GENETICS, Vol: 53, Pages: 128-+, ISSN: 1061-4036

Journal article

Dai C, Wang S, Raynaud H, Mo Y, Angelini E, Guo Y, Bai Wet al., 2021, Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction, Pages: 514-523, ISSN: 0302-9743

Gliomas are among the most common types of malignant brain tumours in adults. Given the intrinsic heterogeneity of gliomas, the multi-parametric magnetic resonance imaging (mpMRI) is the most effective technique for characterising gliomas and their sub-regions. Accurate segmentation of the tumour sub-regions on mpMRI is of clinical significance, which provides valuable information for treatment planning and survival prediction. Thanks to the recent developments on deep learning, the accuracy of automated medical image segmentation has improved significantly. In this paper, we leverage the widely used attention and self-training techniques to conduct reliable brain tumour segmentation and uncertainty estimation. Based on the segmentation result, we present a biophysics-guided prognostic model for the prediction of overall survival. Our method of uncertainty estimation has won the second place of the MICCAI 2020 BraTS Challenge.

Conference paper

Lu P, Bai W, Rueckert D, Noble JAet al., 2021, DYNAMIC SPATIO-TEMPORAL GRAPH CONVOLUTIONAL NETWORKS FOR CARDIAC MOTION ANALYSIS, 18th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 122-125, ISSN: 1945-7928

Conference paper

Lu P, Bai W, Rueckert D, Noble JAet al., 2021, Modelling Cardiac Motion via Spatio-Temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions, Pages: 56-65, ISSN: 0302-9743

We present a novel spatio-temporal graph convolutional networks (ST-GCN) approach to learn spatio-temporal patterns of left ventricular (LV) motion in cardiac MR cine images for improving the characterization of heart conditions. Specifically, a novel GCN architecture is used, where the sample nodes of endocardial and epicardial contours are connected as a graph to represent the myocardial geometry. We show that the ST-GCN can automatically quantify the spatio-temporal patterns in cine MR that characterise cardiac motion. Experiments are performed on healthy volunteers from the UK Biobank dataset. We compare different strategies for constructing cardiac structure graphs. Experiments show that the proposed methods perform well in estimating endocardial radii and characterising cardiac motion features for regional LV analysis.

Conference paper

Kart T, Bai W, Glocker B, Rueckert Det al., 2021, DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization, Pages: 259-267, ISSN: 0302-9743

In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due to the fact that ground-truth labels are time-consuming and expensive to obtain manually. Generating labels from patient metadata might be feasible but it suffers from user-originated errors which introduce biases. In this work, we propose an unsupervised approach for automatically clustering and categorizing large-scale medical image datasets, with a focus on cardiac MR images, and without using any labels. We investigated the end-to-end training using both class-balanced and imbalanced large-scale datasets. Our method was able to create clusters with high purity and achieved over 0.99 cluster purity on these datasets. The results demonstrate the potential of the proposed method for categorizing unstructured large medical databases, such as organizing clinical PACS systems in hospitals.

Conference paper

Xiong Z, Xia Q, Hu Z, Huang N, Bian C, Zheng Y, Vesal S, Ravikumar N, Maier A, Yang X, Heng P-A, Ni D, Li C, Tong Q, Si W, Puybareau E, Khoudli Y, Geraud T, Chen C, Bai W, Rueckert D, Xu L, Zhuang X, Luo X, Jia S, Sermesant M, Liu Y, Wang K, Borra D, Masci A, Corsi C, de Vente C, Veta M, Karim R, Preetha CJ, Engelhardt S, Qiao M, Wang Y, Tao Q, Nunez-Garcia M, Camara O, Savioli N, Lamata P, Zhao Jet al., 2021, A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging, Medical Image Analysis, Vol: 67, Pages: 1-14, ISSN: 1361-8415

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalitie

Journal article

Lu P, Bai W, Rueckert D, Noble JAet al., 2021, Multiscale Graph Convolutional Networks for Cardiac Motion Analysis, Pages: 264-272, ISBN: 9783030787097

We propose a multiscale spatio-temporal graph convolutional network (MST-GCN) approach to learn the left ventricular (LV) motion patterns from cardiac MR image sequences. The MST-GCN follows an encoder-decoder framework. The encoder uses a sequence of multiscale graph computation units (MGCUs). The myocardial geometry is represented as a graph. The network models the internal relations of the graph nodes via feature extraction at different scales and fuses the feature across scales to form a global representation of the input cardiac motion. Based on this, the decoder employs a graph-based gated recurrent unit (G-GRU) to predict future cardiac motion. We show that the MST-GCN can automatically quantify the spatio-temporal patterns in cardiac MR that characterise cardiac motion. Experiments are performed on mid-ventricular short-axis view cardiac MR image sequence from the UK Biobank dataset. We compare the performance of cardiac motion prediction of the proposed method with ten different architectures and parameter settings. Experiments show that the proposed method inputting node positions and node velocities with multiscale graphs achieves the best performance with a mean squared error of 0.25 pixel between the ground truth node locations and our prediction. We also show that the proposed method can estimate a number of motion-related metrics, including endocardial radii, thickness and strain which are useful for regional LV function assessment.

Book chapter

Bai W, Suzuki H, Huang J, Francis C, Wang S, Tarroni G, Guitton F, Aung N, Fung K, Petersen SE, Piechnik SK, Neubauer S, Evangelou E, Dehghan A, O'Regan DP, Wilkins MR, Guo Y, Matthews PM, Rueckert Det al., 2020, A population-based phenome-wide association study of cardiac and aortic structure and function, Nature Medicine, Vol: 26, Pages: 1654-1662, ISSN: 1078-8956

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

Journal article

Qin C, Wang S, Chen C, Qiu H, Bai W, Rueckert Det al., 2020, Biomechanics-informed neural networks for myocardial motion tracking in MRI, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: Springer International Publishing, Pages: 296-306, ISSN: 0302-9743

Image registration is an ill-posed inverse problem which often requires regularisation on the solution space. In contrast to most of the current approaches which impose explicit regularisation terms such as smoothness, in this paper we propose a novel method that can implicitly learn biomechanics-informed regularisation. Such an approach can incorporate application-specific prior knowledge into deep learning based registration. Particularly, the proposed biomechanics-informed regularisation leverages a variational autoencoder (VAE) to learn a manifold for biomechanically plausible deformations and to implicitly capture their underlying properties via reconstructing biomechanical simulations. The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data from two different datasets. The results show that it can achieve better performance against other competing methods in terms of motion tracking accuracy and has the ability to learn biomechanical properties such as incompressibility and strains. The method has also been shown to have better generalisability to unseen domains compared with commonly used L2 regularisation schemes.

Conference paper

Dai C, Wang S, Mo Y, Zhou K, Angelini E, Guo Y, Bai Wet al., 2020, Suggestive annotation of brain tumour images with gradient-guided sampling, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: Springer International Publishing, Pages: 156-165, ISSN: 0302-9743

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven science, the success of machine learning, in particular supervised learning, largely depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire. It takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain tumour images that is able to suggest informative sample images for human experts to annotate. Our experiments show that training a segmentation model with only 19% suggestively annotated patient scans from BraTS 2019 dataset can achieve a comparable performance to training a model on the full dataset for whole tumour segmentation task. It demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.

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, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: Springer Verlag, Pages: 88-97, ISSN: 0302-9743

In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model is deployed into the real clinical world, the model may not perform optimally. A major challenge is the potential poor-quality segmentations generated due to degraded image quality or domain shift issues. There is a timely need to develop an automated quality control method that can detect poor segmentations and feedback to clinicians. Here we propose a novel deep generative model-based framework for quality control of cardiac MRI segmentation. It first learns a manifold of good-quality image-segmentation pairs using a generative model. The quality of a given test segmentation is then assessed by evaluating the difference from its projection onto the good-quality manifold. In particular, the projection is refined through iterative search in the latent space. The proposed method achieves high prediction accuracy on two publicly available cardiac MRI datasets. Moreover, it shows better generalisation ability than traditional regression-based methods. Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.

Conference paper

Zhang D, Lo FP-W, Zheng J-Q, Bai W, Yang G-Z, Lo Bet al., 2020, Data-driven microscopic pose and depth estimation for optical microrobot manipulation, ACS Photonics, Vol: 7, Pages: 3003-3014, ISSN: 2330-4022

Optical microrobots have a wide range of applications in biomedical research for both in vitro and in vivo studies. In most microrobotic systems, the video captured by a monocular camera is the only way for visualizing the movements of microrobots, and only planar motion, in general, can be captured by a monocular camera system. Accurate depth estimation is essential for 3D reconstruction or autofocusing of microplatforms, while the pose and depth estimation are necessary to enhance the 3D perception of the microrobotic systems to enable dexterous micromanipulation and other tasks. In this paper, we propose a data-driven method for pose and depth estimation in an optically manipulated microrobotic system. Focus measurement is used to obtain features for Gaussian Process Regression (GPR), which enables precise depth estimation. For mobile microrobots with varying poses, a novel method is developed based on a deep residual neural network with the incorporation of prior domain knowledge about the optical microrobots encoded via GPR. The method can simultaneously track microrobots with complex shapes and estimate the pose and depth values of the optical microrobots. Cross-validation has been conducted to demonstrate the submicron accuracy of the proposed method and precise pose and depth perception for microrobots. We further demonstrate the generalizability of the method by adapting it to microrobots of different shapes using transfer learning with few-shot calibration. Intuitive visualization is provided to facilitate effective human-robot interaction during micromanipulation based on pose and depth estimation results.

Journal article

Meyer H, Dawes T, Serrani M, Bai W, Tokarczuk P, Cai J, Simoes Monteiro de Marvao A, Henry A, Lumbers T, Gierten J, Thumberger T, Wittbrodt J, Ware J, Rueckert D, Matthews P, Prasad S, Costantino M, Cook S, Birney E, O'Regan Det al., 2020, Genetic and functional insights into the fractal structure of the heart, Nature, Vol: 584, Pages: 589-594, ISSN: 0028-0836

The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a vestigeof embryonic development.1,2 The function of these trabeculae in adults and their genetic architecture are unknown. Toinvestigate this we performed a genome-wide association study using fractal analysis of trabecular morphology as animage-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associatedwith haemodynamic phenotypes and regulation of cytoskeletal arborisation.3,4 Using biomechanical simulations and humanobservational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Throughgenetic association studies with cardiac disease phenotypes and Mendelian randomisation, we find a causal relationshipbetween trabecular morphology and cardiovascular disease risk. These findings suggest an unexpected role for myocardialtrabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity, and reveal theirinfluence on susceptibility to disease

Journal article

Chen C, Qin C, Qiu H, Ouyang C, Wang S, Chen L, Tarroni G, Bai W, Rueckert Det al., 2020, Realistic adversarial data augmentation for MR image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

Neural network-based approaches can achieve high accuracy in various medicalimage segmentation tasks. However, they generally require large labelleddatasets for supervised learning. Acquiring and manually labelling a largemedical dataset is expensive and sometimes impractical due to data sharing andprivacy issues. In this work, we propose an adversarial data augmentationmethod for training neural networks for medical image segmentation. Instead ofgenerating pixel-wise adversarial attacks, our model generates plausible andrealistic signal corruptions, which models the intensity inhomogeneities causedby a common type of artefacts in MR imaging: bias field. The proposed methoddoes not rely on generative networks, and can be used as a plug-in module forgeneral segmentation networks in both supervised and semi-supervised learning.Using cardiac MR imaging we show that such an approach can improve thegeneralization ability and robustness of models as well as provide significantimprovements in low-data scenarios.

Conference paper

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

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