680 results found
Schlemper J, Oktay O, Schaap M, et al., 2019, Attention gated networks: Learning to leverage salient regions in medical images, MEDICAL IMAGE ANALYSIS, Vol: 53, Pages: 197-207, ISSN: 1361-8415
Alansary A, Oktay O, Li Y, et al., 2019, Evaluating reinforcement learning agents for anatomical landmark detection, MEDICAL IMAGE ANALYSIS, Vol: 53, Pages: 156-164, ISSN: 1361-8415
Bruun M, Frederiksen KS, Rhodius-Meester HFM, et al., 2019, Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study, ALZHEIMERS RESEARCH & THERAPY, Vol: 11, ISSN: 1758-9193
Robinson R, Valindria VV, Bai W, et al., 2019, Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 21, ISSN: 1097-6647
Dawes TJW, de Marvao A, Shi W, et al., 2019, Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance study, ANAESTHESIA, Vol: 74, Pages: 312-320, ISSN: 0003-2409
van Essen TA, den Boogert HF, Cnossen MC, et al., 2019, Variation in neurosurgical management of traumatic brain injury: a survey in 68 centers participating in the CENTER-TBI study (vol 161, pg 453, 2019), ACTA NEUROCHIRURGICA, Vol: 161, Pages: 451-455, ISSN: 0001-6268
Lavdas I, Glocker B, Rueckert D, et al., 2019, Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data., Clin Radiol
Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project, which aims to develop machine learning methods to improve the diagnostic performance and reduce the radiology reading time of whole-body magnetic resonance imaging (MRI) scans, in patients being staged for cancer (MALIBO study). We describe here the main challenges we have encountered during the course of this project. Data quality and uniformity are the two most important data traits to be considered in clinical trials incorporating machine learning. Robust data pre-processing and machine learning pipelines have been employed in MALIBO, a task facilitated by the now freely available machine learning libraries and toolboxes. Another important consideration for achieving the desired clinical outcome in MALIBO, was to effectively host the resulting machine learning output, along with the clinical images, for reading in a clinical environment. Finally, a range of legal, ethical, and clinical acceptance issues should be considered when attempting to incorporate computer-assisting tools into clinical practice. The road from translating computational methods into potentially useful clinical tools involves an analytical, stepwise adaptation approach, as well as engagement of a multidisciplinary team.
Howard J, Fisher L, Shun-Shin M, et al., Cardiac rhythm device identification using neural networks, JACC: Clinical Electrophysiology, ISSN: 2405-5018
BackgroundMedical staff often need to determine the model of a pacemaker or defibrillator (cardiac rhythm devices) quickly and accurately. Current approaches involve comparing a device’s X-ray appearance with a manual flow chart. We aimed to see whether a neural network could be trained to perform this task more accurately.Methods and ResultsWe extracted X-ray images of 1676 devices, comprising 45 models from 5 manufacturers. We developed a convolutional neural network to classify the images, using a training set of 1451 images. The testing set was a further 225 images, consisting of 5 examples of each model. We compared the network’s ability to identify the manufacturer of a device with those of cardiologists using a published flow-chart.The neural network was 99.6% (95% CI 97.5 to 100) accurate in identifying the manufacturer of a device from an X-ray, and 96.4% (95% CI 93.1 to 98.5) accurate in identifying the model group. Amongst 5 cardiologists using the flow-chart, median manufacturer accuracy was 72.0% (range 62.2% to 88.9%), and model group identification was not possible. The network was significantly superior to all of the cardiologists in identifying the manufacturer (p < 0.0001 against the median human; p < 0.0001 against the best human).ConclusionsA neural network can accurately identify the manufacturer and even model group of a cardiac rhythm device from an X-ray, and exceeds human performance. This system may speed up the diagnosis and treatment of patients with cardiac rhythm devices and it is publicly accessible online.
Bello GA, Dawes TJW, Duan J, et al., 2019, Deep learning cardiac motion analysis for human survival prediction., Nat Mach Intell, Vol: 1, Pages: 95-104, ISSN: 2522-5839
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 = .0012) for our model C=0.75 (95% CI: 0.70 - 0.79) 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.
Gilbert K, Bai W, Mauger C, et al., 2019, Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study, SCIENTIFIC REPORTS, Vol: 9, ISSN: 2045-2322
Duan J, Bello G, Schlemper J, et al., 2019, Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach., IEEE Trans Med Imaging
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the presence of artefacts in input CMR volumes.
Bastiani M, Andersson JLR, Cordero-Grande L, et al., 2019, Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project, NEUROIMAGE, Vol: 185, Pages: 750-763, ISSN: 1053-8119
Qin C, Schlemper J, Caballero J, et al., 2019, Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 38, Pages: 280-290, ISSN: 0278-0062
Bruun M, Frederiksen KS, Rhodius-Meester HFM, et al., 2019, Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study., Curr Alzheimer Res, Vol: 16, Pages: 91-101
BACKGROUND: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. OBJECTIVES: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. METHODS: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. RESULTS: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). CONCLUSION: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians' confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.
Balaban G, Halliday BP, Costa CM, et al., 2018, Fibrosis Microstructure Modulates Reentry in Non-ischemic Dilated Cardiomyopathy: Insights From Imaged Guided 2D Computational Modeling, FRONTIERS IN PHYSIOLOGY, Vol: 9, ISSN: 1664-042X
Attard MI, Dawes TJW, de Marvao A, et al., 2018, Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging., Eur Heart J Cardiovasc Imaging
Aims: We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results: In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function-including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = -0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28-0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = -0.40) of sulfated androgen. Conclusion: Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.
Cox DJ, Bai W, Price AN, et al., 2018, Ventricular remodeling in preterm infants: computational cardiac magnetic resonance atlasing shows significant early remodeling of the left ventricle., Pediatr Res
BACKGROUND: Premature birth is associated with ventricular remodeling, early heart failure, and altered left ventricular (LV) response to physiological stress. Using computational cardiac magnetic resonance (CMR) imaging, we aimed to quantify preterm ventricular remodeling in the neonatal period, and explore contributory clinical factors. METHODS: Seventy-three CMR scans (34 preterm infants, 10 term controls) were performed to assess in-utero development and preterm ex-utero growth. End-diastolic computational atlases were created for both cardiac ventricles; t statistics, linear regression modeling, and principal component analysis (PCA) were used to describe the impact of prematurity and perinatal factors on ventricular volumetrics, ventricular geometry, myocardial mass, and wall thickness. RESULTS: All preterm neonates demonstrated greater weight-indexed LV mass and higher weight-indexed end-diastolic volume at term-corrected age (P < 0.05 for all preterm gestations). Independent associations of increased term-corrected age LV myocardial wall thickness were (false discovery rate <0.05): degree of prematurity, antenatal glucocorticoid administration, and requirement for >48 h postnatal respiratory support. PCA of LV geometry showed statistical differences between all preterm infants at term-corrected age and term controls. CONCLUSIONS: Computational CMR demonstrates that significant LV remodeling occurs soon after preterm delivery and is associated with definable clinical situations. This suggests that neonatal interventions could reduce long-term cardiac dysfunction.
Tarroni G, Oktay O, Bai W, et al., 2018, Learning-Based Quality Control for Cardiac MR Images., IEEE Trans Med Imaging
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
Bozek J, Makropoulos A, Schuh A, et al., 2018, Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project, NEUROIMAGE, Vol: 179, Pages: 11-29, ISSN: 1053-8119
Bai W, Sinclair M, Tarroni G, et al., 2018, Automated cardiovascular magnetic resonance image analysis with fully convolutional networks, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 20, ISSN: 1097-6647
Bhuva A, Treibel TA, De Marvao A, et al., 2018, Septal hypertrophy in aortic stenosis and its regression after valve replacement is more plastic in males than females: insights from 3D machine learning approach, European-Society-of-Cardiology Congress, Publisher: OXFORD UNIV PRESS, Pages: 1132-1132, ISSN: 0195-668X
Hou B, Khanal B, Alansary A, et al., 2018, 3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 1737-1750, ISSN: 0278-0062
Parisot S, Ktena SI, Ferrante E, et al., 2018, Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease, MEDICAL IMAGE ANALYSIS, Vol: 48, Pages: 117-130, ISSN: 1361-8415
Chen L, Jones ALC, Mair G, et al., 2018, Rapid Automated Quantification of Cerebral Leukoaraiosis on CT Images: A Multicenter Validation Study, RADIOLOGY, Vol: 288, Pages: 573-581, ISSN: 0033-8419
Ledig C, Schuh A, Guerrero R, et al., 2018, Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database, SCIENTIFIC REPORTS, Vol: 8, ISSN: 2045-2322
Dawes T, Serrani M, Bai W, et al., 2018, Myocardial trabeculae improve left ventricular function: a combined UK Biobank and computational analysis, AAGBI GAT Annual Scientific Meeting, Publisher: WILEY, Pages: 12-12, ISSN: 0003-2409
Koch LM, Rajchl M, Bai W, et al., 2018, Multi-Atlas Segmentation Using Partially Annotated Data: Methods and Annotation Strategies, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 40, Pages: 1683-1696, ISSN: 0162-8828
Cerrolaza JJ, Li Y, Biffi C, et al., 2018, Fetal Skull Reconstruction via Deep Convolutional Autoencoders., Conf Proc IEEE Eng Med Biol Soc, Vol: 2018, Pages: 887-890, ISSN: 1557-170X
Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest. In this paper, we present two deep convolutional architectures for the reconstruction of the fetal skull in partially occluded 3DUS volumes: a TL deep convolutional network (TL-Net), and a conditional variational autoencoder (CVAE). The performance of the two networks was evaluated for occlusion rates up to 50%, both showing accurate results even when only 60% of the skull is included in the US volume (Dice coeff. $0.84\pm 0.04$ for CVAE and $0.83\pm 0.03$ for TL-Net). The reconstruction networks proposed here have the potential to optimize image acquisition protocols in obstetric sonography, reducing the acquisition time and providing comprehensive anatomical information even from partially occluded images.
Sinclair M, Baumgartner CF, Matthew J, et al., 2018, Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks., Conf Proc IEEE Eng Med Biol Soc, Vol: 2018, Pages: 714-717, ISSN: 1557-170X
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model's performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers.
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