817 results found
Sewalt CA, Gravesteijn BY, Menon D, et al., 2021, Primary versus early secondary referral to a specialized neurotrauma center in patients with moderate/severe traumatic brain injury: a CENTER TBI study, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, Vol: 29
Background: Prehospital care for patients with traumatic brain injury (TBI) varies with some emergency medical systems recommending direct transport of patients with moderate to severe TBI to hospitals with specialist neurotrauma care (SNCs). The aim of this study is to assess variation in levels of early secondary referral within European SNCs and to compare the outcomes of directly admitted and secondarily transferred patients. Methods: Patients with moderate and severe TBI (Glasgow Coma Scale < 13) from the prospective European CENTER-TBI study were included in this study. All participating hospitals were specialist neuroscience centers. First, adjusted between-country differences were analysed using random effects logistic regression where early secondary referral was the dependent variable, and a random intercept for country was included. Second, the adjusted effect of early secondary referral on survival to hospital discharge and functional outcome [6 months Glasgow Outcome Scale Extended (GOSE)] was estimated using logistic and ordinal mixed effects models, respectively. Results: A total of 1347 moderate/severe TBI patients from 53 SNCs in 18 European countries were included. Of these 1347 patients, 195 (14.5%) were admitted after early secondary referral. Secondarily referred moderate/severe TBI patients presented more often with a CT abnormality: mass lesion (52% vs. 34%), midline shift (54% vs. 36%) and acute subdural hematoma (77% vs. 65%). After adjusting for case-mix, there was a large European variation in early secondary referral, with a median OR of 1.69 between countries. Early secondary referral was not associated with functional outcome (adjusted OR 1.07, 95% CI 0.78–1.69), nor with survival at discharge (1.05, 0.58–1.90). Conclusions: Across Europe, substantial practice variation exists in the proportion of secondarily referred TBI patients at SNCs that is not explained by case mix. Within SNCs early secondary referral does no
Dimitrova R, Pietsch M, Ciarrusta J, et al., 2021, Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age, NeuroImage, Vol: 243, ISSN: 1053-8119
INTRODUCTION: The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS: We sought to first map typical cortical micro and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n=259). Then we offer a comprehensive characterisation of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS: In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION: We showed that preterm birth alters cortical micro and macrostructural maturation near
Chen C, Hammernik K, Ouyang C, et al., 2021, Cooperative training and latent space data augmentation for robust medical image segmentation, International Conference on Medical Image Computing and Computer Assisted Intervention
Wiegers EJA, Lingsma HF, Huijben JA, et al., 2021, Fluid balance and outcome in critically ill patients with traumatic brain injury (CENTER-TBI and OzENTER-TBI): a prospective, multicentre, comparative effectiveness study, The Lancet Neurology, Vol: 20, Pages: 627-638, ISSN: 1474-4422
Background: Fluid therapy—the administration of fluids to maintain adequate organ tissue perfusion and oxygenation—is essential in patients admitted to the intensive care unit (ICU) with traumatic brain injury. We aimed to quantify the variability in fluid management policies in patients with traumatic brain injury and to study the effect of this variability on patients' outcomes. Methods: We did a prospective, multicentre, comparative effectiveness study of two observational cohorts: CENTER-TBI in Europe and OzENTER-TBI in Australia. Patients from 55 hospitals in 18 countries, aged 16 years or older with traumatic brain injury requiring a head CT, and admitted to the ICU were included in this analysis. We extracted data on demographics, injury, and clinical and treatment characteristics, and calculated the mean daily fluid balance (difference between fluid input and loss) and mean daily fluid input during ICU stay per patient. We analysed the association of fluid balance and input with ICU mortality and functional outcome at 6 months, measured by the Glasgow Outcome Scale Extended (GOSE). Patient-level analyses relied on adjustment for key characteristics per patient, whereas centre-level analyses used the centre as the instrumental variable. Findings: 2125 patients enrolled in CENTER-TBI and OzENTER-TBI between Dec 19, 2014, and Dec 17, 2017, were eligible for inclusion in this analysis. The median age was 50 years (IQR 31 to 66) and 1566 (74%) of patients were male. The median of the mean daily fluid input ranged from 1·48 L (IQR 1·12 to 2·09) to 4·23 L (3·78 to 4·94) across centres. The median of the mean daily fluid balance ranged from −0·85 L (IQR −1·51 to −0·49) to 1·13 L (0·99 to 1·37) across centres. In patient-level analyses, a mean positive daily fluid balance was associated with higher ICU mortality (odds ratio [OR] 1·10 [95% CI 1&midd
Carney O, Hughes E, Tusor N, et al., 2021, Incidental findings on brain MR imaging of asymptomatic term neonates in the Developing Human Connectome Project, ECLINICALMEDICINE, Vol: 38
Kamnitsas K, Winzeck S, Kornaropoulos EN, et al., 2021, Transductive image segmentation: Self-training and effect of uncertainty estimation, MICCAI Workshop on Domain Adaptation and Representation Transfer
Semi-supervised learning (SSL) uses unlabeled data during training to learnbetter models. Previous studies on SSL for medical image segmentation focusedmostly on improving model generalization to unseen data. In some applications,however, our primary interest is not generalization but to obtain optimalpredictions on a specific unlabeled database that is fully available duringmodel development. Examples include population studies for extracting imagingphenotypes. This work investigates an often overlooked aspect of SSL,transduction. It focuses on the quality of predictions made on the unlabeleddata of interest when they are included for optimization during training,rather than improving generalization. We focus on the self-training frameworkand explore its potential for transduction. We analyze it through the lens ofInformation Gain and reveal that learning benefits from the use of calibratedor under-confident models. Our extensive experiments on a large MRI databasefor multi-class segmentation of traumatic brain lesions shows promising resultswhen comparing transductive with inductive predictions. We believe this studywill inspire further research on transductive learning, a well-suited paradigmfor medical image analysis.
Qin C, Duan J, Hammernik K, et al., 2021, Complementary time-frequency domain networks for dynamic parallel MR image reconstruction, MAGNETIC RESONANCE IN MEDICINE, ISSN: 0740-3194
Chai S, Rueckert D, Fetit A, 2021, Reducing textural bias improves robustness of deep segmentation models, Annual Conference on Medical Image Understanding and Analysis (MIUA 2021), Publisher: Springer Verlag, Pages: 294-304, ISSN: 0302-9743
Despite advances in deep learning, robustness under domain shift remains a major bottleneck in medical imaging settings. Findings on natural images suggest that deep neural models can show a strong textural bias when carrying out image classification tasks. In this thorough empirical study, we draw inspiration from findings on natural images and investigate ways in which addressing the textural bias phenomenon could bring up the robustness of deep segmentation models when applied to three-dimensional (3D) medical data. To achieve this, publicly available MRI scans from the Developing Human Connectome Project are used to study ways in which simulating textural noise can help train robust models in a complex semantic segmentation task. We contribute an extensive empirical investigation consisting of 176 experiments and illustrate how applying specific types of simulated textural noise prior to training can lead to texture invariant models, resulting in improved robustness when segmenting scans corrupted by previously unseen noise types and levels.
Simoes Monteiro de Marvao A, McGurk K, Zheng S, et al., 2021, Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy, Journal of the American College of Cardiology, 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.
Eyre M, Fitzgibbon SP, Ciarrusta J, et al., 2021, Erratum to: The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity., Brain
Eyre M, Fitzgibbon SP, Ciarrusta J, et al., 2021, The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity, Brain: a journal of neurology, Vol: 144, Pages: 2199-2213, ISSN: 0006-8950
The Developing Human Connectome Project (dHCP) is an Open Science project which provides the first large sample of neonatal functional MRI (fMRI) data with high temporal and spatial resolution. This data enables mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organisation in humans. Here, we characterise in unprecedented detail the maturation and integrity of resting-state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm). First, we applied group independent component analysis (ICA) to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder which occur frequently in preterm children. Overall, we find a robust, modular
Ziller A, Usynin D, Braren R, et al., 2021, Medical imaging deep learning with differential privacy, Scientific Reports, Vol: 11, ISSN: 2045-2322
The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical requirements of the medical profession. Differential privacy (DP) enables the provision of information-theoretic privacy guarantees to patients and can be implemented in the setting of deep neural network training through the differentially private stochastic gradient descent (DP-SGD) algorithm. We here present deepee, a free-and-open-source framework for differentially private deep learning for use with the PyTorch deep learning framework. Our framework is based on parallelised execution of neural network operations to obtain and modify the per-sample gradients. The process is efficiently abstracted via a data structure maintaining shared memory references to neural network weights to maintain memory efficiency. We furthermore offer specialised data loading procedures and privacy budget accounting based on the Gaussian Differential Privacy framework, as well as automated modification of the user-supplied neural network architectures to ensure DP-conformity of its layers. We benchmark our framework's computational performance against other open-source DP frameworks and evaluate its application on the paediatric pneumonia dataset, an image classification task and on the Medical Segmentation Decathlon Liver dataset in the task of medical image segmentation. We find that neural network training with rigorous privacy guarantees is possible while maintaining acceptable classification performance and excellent segmentation performance. Our framework compares favourably to related work with respect to memory consumption and computational performance. Our work presents an open-source software framework for differentially private deep learning, which we demonstrate in medical imaging analysis tasks. It serves to further th
Hammernik K, Schlemper J, Qin C, et al., 2021, Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination, MAGNETIC RESONANCE IN MEDICINE, ISSN: 0740-3194
Kart T, Fischer M, Kuestner T, et al., 2021, Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies, INVESTIGATIVE RADIOLOGY, Vol: 56, Pages: 401-408, ISSN: 0020-9996
Kaissis G, Ziller A, Passerat-Palmbach J, et al., 2021, End-to-end privacy preserving deep learning on multi-institutional medical imaging, NATURE MACHINE INTELLIGENCE, Vol: 3, Pages: 473-484
Zhou SK, Greenspan H, Davatzikos C, et al., 2021, A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises, PROCEEDINGS OF THE IEEE, Vol: 109, Pages: 820-838, ISSN: 0018-9219
Lu P, Bai W, Rueckert D, et al., 2021, Dynamic spatio-temporal graph convolutional networks for cardiac motion analysis, Pages: 122-125, ISSN: 1945-7928
We propose a dynamic spatio-temporal graph convolutional network (DST-GCN) approach to learn the left ventricular (LV) motion patterns from cardiac MR cine images. We represent the myocardial geometry using a graph that is constructed from sample nodes on endo-and epicardial contours. The DST-GCN follows an encoder-decoder framework. The encoder accepts a given cardiac motion represented by a sequence of ST-GCN. The decoder employs a graph-based gated recurrent unit (G-GRU) to predict future cardiac motion. We show that the DST-GCN can automatically quantify the spatio-temporal patterns in cardiac MR that characterise cardiac motion. Experiments are performed on the UK Biobank dataset. We compare four methods from two architecture variances. Experiments show that the proposed method inputting node velocities with residual connection in the decoder outperform others, and achieves a mean squared error of 0.135 pixel between the ground truth node locations and our prediction.
Hassan ON, Menten MJ, Bogunovic H, et al., 2021, Deep learning prediction of age and sex from optical coherence tomography, Pages: 238-242, ISSN: 1945-7928
Convolutional neural networks (CNNs) have achieved remarkable success in predicting clinical information and individuals' characteristics from medical images. Previous ophthalmological studies have suggested that age and sex have retinal manifestations that can be observed in retinal optical coherence tomography (OCT) scans. Following on these studies, we evaluated the use of three-dimensional CNNs for predicting the subject's age and sex directly from 3D retinal OCT scans. We also assessed the effect of the receptive field size on the model performance. In addition, we adopted a robust and simple bias-adjustment scheme for further performance enhancement of eye age prediction. We used a large dataset consisting of 66, 767 subjects with OCT scans from the UK Biobank data and evaluated our model on 10% of the dataset (i.e. 6, 676 subjects). An accurate prediction was obtained for age (mean absolute error (MAE): 3.3 years, coefficient of determination R2: 0.89) while an acceptable performance was achieved for sex (area under the curve (AUC): 0.86).
Dimitrova R, Arulkumaran S, Carney O, et al., 2021, Phenotyping the preterm brain: characterizing individual deviations from normative volumetric development in two large infant cohorts, Cerebral Cortex, Vol: 31, Pages: 3665-3677, ISSN: 1047-3211
The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterizing brain volumetric development in 274 term-born infants, modeling for age at scan and sex. We then compared 89 preterm infants scanned at term-equivalent age with these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalizability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, nonuniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguises a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterization of the cerebral consequences of preterm birth by profiling the individual neonatal brain.
Dou Q, So TY, Jiang M, et al., 2021, Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study, NPJ DIGITAL MEDICINE, Vol: 4, ISSN: 2398-6352
Meng Q, Matthew J, Zimmer VA, et al., 2021, Mutual information-based disentangled neural networks for classifying unseen categories in different domains: application to fetal ultrasound imaging, IEEE Transactions on Medical Imaging, Vol: 40, Pages: 722-734, ISSN: 0278-0062
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an interesting and difficult challenge. This problem occurs frequently in medical imaging applications when attempts are made to deploy and improve deep learning models across different image acquisition devices, across acquisition parameters or if some classes are unavailable in new training databases. To ad-dress this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain. The proposed MID-Net adopts a semi-supervised learning paradigm to alleviate the dependency on labeled data. This is important for real-world applications where data annotation is time-consuming, costly and requires training and expertise. We extensively evaluate the proposed method on fetal ultra-sound datasets for two different image classification tasks where domain features are respectively defined by shadow artifacts and image acquisition devices. Experimental results show that the proposed method outperforms the state-of-the-art on the classification of unseen categories in a target domain with sparsely labeled training data.
Balaban G, Halliday B, Bradley P, et 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.
de Marvao A, McGurk KA, Zheng SL, et 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 <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<0.001), mainly due to heart failure (HR 4.40, 95% CI 3.22-6.02, p<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<0.001) and concentric remodelling (mass/volume ratio: 0.63±0.12 vs 0.58±0.09 g/mL, p<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
Knolle M, Kaissis G, Jungmann F, et al., 2021, Efficient, high-performance semantic segmentation using multi-scale feature extraction, PLOS ONE, Vol: 16, ISSN: 1932-6203
Budd S, Sinclair M, Day T, et al., 2021, Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the earlydetection of fetal malformations which have potential long-term health impacts.The level of skill required to diagnose such malformations from live ultrasoundduring examination is high and resources for screening are often limited. Wepresent an interpretable, atlas-learning segmentation method for automaticdiagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single `4 ChamberHeart' view image. We propose to extend the recently introducedImage-and-Spatial Transformer Networks (Atlas-ISTN) into a framework thatenables sensitising atlas generation to disease. In this framework we canjointly learn image segmentation, registration, atlas construction and diseaseprediction while providing a maximum level of clinical interpretabilitycompared to direct image classification methods. As a result our segmentationallows diagnoses competitive with expert-derived manual diagnosis and yields anAUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 fortesting).
Lu P, Bai W, Rueckert D, et 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.
Lu P, Bai W, Rueckert D, et 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.
Xiong Z, Xia Q, Hu Z, et 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
Andelic N, Røe C, Brunborg C, et al., 2021, Frequency of fatigue and its changes in the first 6 months after traumatic brain injury: results from the CENTER-TBI study., J Neurol, Vol: 268, Pages: 61-73
BACKGROUND: Fatigue is one of the most commonly reported subjective symptoms following traumatic brain injury (TBI). The aims were to assess frequency of fatigue over the first 6 months after TBI, and examine whether fatigue changes could be predicted by demographic characteristics, injury severity and comorbidities. METHODS: Patients with acute TBI admitted to 65 trauma centers were enrolled in the study Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI). Subjective fatigue was measured by single item on the Rivermead Post-Concussion Symptoms Questionnaire (RPQ), administered at baseline, three and 6 months postinjury. Patients were categorized by clinical care pathway: admitted to an emergency room (ER), a ward (ADM) or an intensive care unit (ICU). Injury severity, preinjury somatic- and psychiatric conditions, depressive and sleep problems were registered at baseline. For prediction of fatigue changes, descriptive statistics and mixed effect logistic regression analysis are reported. RESULTS: Fatigue was experienced by 47% of patients at baseline, 48% at 3 months and 46% at 6 months. Patients admitted to ICU had a higher probability of experiencing fatigue than those in ER and ADM strata. Females and individuals with lower age, higher education, more severe intracranial injury, preinjury somatic and psychiatric conditions, sleep disturbance and feeling depressed postinjury had a higher probability of fatigue. CONCLUSION: A high and stable frequency of fatigue was found during the first 6 months after TBI. Specific socio-demographic factors, comorbidities and injury severity characteristics were predictors of fatigue in this study.
Andelic N, Røe C, Brunborg C, et al., 2021, Correction to: Frequency of fatigue and its changes in the first 6 months after traumatic brain injury: results from the CENTER-TBI study., J Neurol, Vol: 268, Pages: 74-76
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