744 results found
Tournier J-D, Christiaens D, Hutter J, et al., 2020, A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging, NMR IN BIOMEDICINE, ISSN: 0952-3480
Fenchel D, Dimitrova R, Seidlitz J, et al., 2020, Development of microstructural and morphological cortical profiles in the neonatal brain, Cerebral Cortex, ISSN: 1047-3211
Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37-44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory-motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.
Wang S, Tarroni G, Qin C, et al., 2020, Deep generative model-based quality control for cardiac MRI segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
In recent years, convolutional neural networks have demonstrated promisingperformance in a variety of medical image segmentation tasks. However, when atrained segmentation model is deployed into the real clinical world, the modelmay not perform optimally. A major challenge is the potential poor-qualitysegmentations generated due to degraded image quality or domain shift issues.There is a timely need to develop an automated quality control method that candetect poor segmentations and feedback to clinicians. Here we propose a noveldeep generative model-based framework for quality control of cardiac MRIsegmentation. It first learns a manifold of good-quality image-segmentationpairs using a generative model. The quality of a given test segmentation isthen assessed by evaluating the difference from its projection onto thegood-quality manifold. In particular, the projection is refined throughiterative search in the latent space. The proposed method achieves highprediction accuracy on two publicly available cardiac MRI datasets. Moreover,it shows better generalisation ability than traditional regression-basedmethods. Our approach provides a real-time and model-agnostic quality controlfor cardiac MRI segmentation, which has the potential to be integrated intoclinical image analysis workflows.
Monteiro M, Newcombe VFJ, Mathieu F, et al., 2020, Multi-class semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning – an algorithm development and multi-centre validation study, The Lancet. Digital Health, Vol: 2, Pages: e314-e322, ISSN: 2589-7500
Background CT is the most common imaging modality in traumatic brain injury (TBI). However, its conventional userequires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognosticimportance. We aimed to use deep learning to reliably and efficiently quantify and detect different lesion types.Methods Patients were recruited between Dec 9, 2014, and Dec 17, 2017, in 60 centres across Europe. We trained andvalidated an initial convolutional neural network (CNN) on expert manual segmentations (dataset 1). This CNN wasused to automatically segment a new dataset of scans, which we then corrected manually (dataset 2). From thisdataset, we used a subset of scans to train a final CNN for multiclass, voxel-wise segmentation of lesion types. Theperformance of this CNN was evaluated on a test subset. Performance was measured for lesion volume quantification,lesion progression, and lesion detection and lesion volume classification. For lesion detection, external validation wasdone on an independent set of 500 patients from India.Findings 98 scans from one centre were included in dataset 1. Dataset 2 comprised 839 scans from 38 centres:184 scans were used in the training subset and 655 in the test subset. Compared with manual reference, CNN-derivedlesion volumes showed a mean difference of 0·86 mL (95% CI –5·23 to 6·94) for intraparenchymal haemorrhage,1·83 mL (–12·01 to 15·66) for extra-axial haemorrhage, 2·09 mL (–9·38 to 13·56) for perilesional oedema, and0·07 mL (–1·00 to 1·13) for intraventricular haemorrhage.Interpretation We show the ability of a CNN to separately segment, quantify, and detect multiclass haemorrhagiclesions and perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification oflesion burden and progression, with potential applications for personalised treatment strategies
Biffi C, Cerrolaza Martinez JJ, Tarroni G, et 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.
Gale-Grant O, Christiaens D, Cordero-Grande L, et al., 2020, Parental age effects on neonatal white matter development., NeuroImage: Clinical, Vol: 27, Pages: 1-7, ISSN: 2213-1582
OBJECTIVE: Advanced paternal age is associated with poor offspring developmental outcome. Though an increase in paternal age-related germline mutations may affect offspring white matter development, outcome differences could also be due to psychosocial factors. Here we investigate possible cerebral changes prior to strong environmental influences using brain MRI in a cohort of healthy term-born neonates. METHODS: We used structural and diffusion MRI images acquired soon after birth from a cohort (n = 275) of healthy term-born neonates. Images were analysed using a customised tract based spatial statistics (TBSS) processing pipeline. Neurodevelopmental assessment using the Bayley-III scales was offered to all participants at age 18 months. For statistical analysis neonates were compared in two groups, representing the upper quartile (paternal age ≥38 years) and lower three quartiles. The same method was used to assess associations with maternal age. RESULTS: In infants with older fathers (≥38 years), fractional anisotropy, a marker of white matter organisation, was significantly reduced in three early maturing anatomical locations (the corticospinal tract, the corpus callosum, and the optic radiation). Fractional anisotropy in these locations correlated positively with Bayley-III cognitive composite score at 18 months in the advanced paternal age group. A small but significant reduction in total brain volume was also observed in in the infants of older fathers. No significant associations were found between advanced maternal age and neonatal imaging. CONCLUSIONS: The epidemiological association between advanced paternal age and offspring outcome is extremely robust. We have for the first time demonstrated a neuroimaging phenotype of advanced paternal age before sustained parental interaction that correlates with later outcome.
van Wijk RPJ, van Dijck JTJM, Timmers M, et al., 2020, Informed consent procedures in patients with an acute inability to provide informed consent: Policy and practice in the CENTER-TBI study., J Crit Care, Vol: 59, Pages: 6-15
PURPOSE: Enrolling traumatic brain injury (TBI) patients with an inability to provide informed consent in research is challenging. Alternatives to patient consent are not sufficiently embedded in European and national legislation, which allows procedural variation and bias. We aimed to quantify variations in informed consent policy and practice. METHODS: Variation was explored in the CENTER-TBI study. Policies were reported by using a questionnaire and national legislation. Data on used informed consent procedures were available for 4498 patients from 57 centres across 17 European countries. RESULTS: Variation in the use of informed consent procedures was found between and within EU member states. Proxy informed consent (N = 1377;64%) was the most frequently used type of consent in the ICU, followed by patient informed consent (N = 426;20%) and deferred consent (N = 334;16%). Deferred consent was only actively used in 15 centres (26%), although it was considered valid in 47 centres (82%). CONCLUSIONS: Alternatives to patient consent are essential for TBI research. While there seems to be concordance amongst national legislations, there is regional variability in institutional practices with respect to the use of different informed consent procedures. Variation could be caused by several reasons, including inconsistencies in clear legislation or knowledge of such legislation amongst researchers.
Meyer H, Dawes T, Serrani M, et al., Genetic and functional insights into the fractal structure of the heart, Nature, 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
Chen C, Bai W, Davies R, et al., Improving the generalizability of convolutional neural network-based segmentation on CMR images, Frontiers in Cardiovascular Medicine, ISSN: 2297-055X
Monteiro M, Kamnitsas K, Ferrante E, et al., 2019, TBI lesion segmentation in head CT: impact of preprocessing and data augmentation, MICCAI Brain Lesion Workshop, Publisher: Springer Verlag, ISSN: 0302-9743
Automatic segmentation of lesions in head CT provides keyinformation for patient management, prognosis and disease monitoring.Despite its clinical importance, method development has mostly focusedon multi-parametric MRI. Analysis of the brain in CT is challengingdue to limited soft tissue contrast and its mono-modal nature. We studythe under-explored problem of fine-grained CT segmentation of multiplelesion types (core, blood, oedema) in traumatic brain injury (TBI). Weobserve that preprocessing and data augmentation choices greatly impactthe segmentation accuracy of a neural network, yet these factors arerarely thoroughly assessed in prior work. We design an empirical studythat extensively evaluates the impact of different data preprocessing andaugmentation methods. We show that these choices can have an impactof up to 18% DSC. We conclude that resampling to isotropic resolutionyields improved performance, skull-stripping can be replaced by using theright intensity window, and affine-to-atlas registration is not necessaryif we use sufficient spatial augmentation. Since both skull-stripping andaffine-to-atlas registration are susceptible to failure, we recommend theiralternatives to be used in practice. We believe this is the first work toreport results for fine-grained multi-class segmentation of TBI in CT. Ourfindings may inform further research in this under-explored yet clinicallyimportant task of automatic head CT lesion segmentation.
Jaubert O, Cruz G, Bustin A, et al., 2020, Free-running cardiac magnetic resonance fingerprinting: Joint T1/T2 map and Cine imaging, MAGNETIC RESONANCE IMAGING, Vol: 68, Pages: 173-182, ISSN: 0730-725X
Dimitrova R, Pietsch M, Christiaens D, et al., 2020, Heterogeneity in Brain Microstructural Development Following Preterm Birth., Cereb Cortex
Preterm-born children are at increased risk of lifelong neurodevelopmental difficulties. Group-wise analyses of magnetic resonance imaging show many differences between preterm- and term-born infants but do not reliably predict neurocognitive prognosis for individual infants. This might be due to the unrecognized heterogeneity of cerebral injury within the preterm group. This study aimed to determine whether atypical brain microstructural development following preterm birth is significantly variable between infants. Using Gaussian process regression, a technique that allows a single-individual inference, we characterized typical variation of brain microstructure using maps of fractional anisotropy and mean diffusivity in a sample of 270 term-born neonates. Then, we compared 82 preterm infants to these normative values to identify brain regions with atypical microstructure and relate observed deviations to degree of prematurity and neurocognition at 18 months. Preterm infants showed strikingly heterogeneous deviations from typical development, with little spatial overlap between infants. Greater and more extensive deviations, captured by a whole brain atypicality index, were associated with more extreme prematurity and predicted poorer cognitive and language abilities at 18 months. Brain microstructural development after preterm birth is highly variable between individual infants. This poorly understood heterogeneity likely relates to both the etiology and prognosis of brain injury.
Pitkanen J, Koikkalainen J, Nieminen T, et al., 2020, Evaluating severity of white matter lesions from computed tomography images with convolutional neural network, NEURORADIOLOGY, ISSN: 0028-3940
Bhuva AN, Treibel TA, De Marvao A, et al., 2020, Sex and regional differences inmyocardial plasticity in aortic stenosis are revealed by 3D modelmachine learning, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 21, Pages: 417-427, ISSN: 2047-2404
Howard JP, Tan J, Shun-Shin MJ, et al., 2020, Improving ultrasound video classification: an evaluation of novel deep learning methods in echocardiography., J Med Artif Intell, Vol: 3
Echocardiography is the commonest medical ultrasound examination, but automated interpretation is challenging and hinges on correct recognition of the 'view' (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs), but these merely classify individual frames of a video in isolation, and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures, including time-distributed networks and two-stream networks, which are inspired by advances in human action recognition. We demonstrate that these new architectures more than halve the error rate of traditional CNNs from 8.1% to 3.9%. These advances in accuracy may be due to these networks' ability to track the movement of specific structures such as heart valves throughout the cardiac cycle. Finally, we show the accuracies of these new state-of-the-art networks are approaching expert agreement (3.6% discordance), with a similar pattern of discordance between views.
Onofrey JA, Staib LH, Huang X, et al., 2020, Sparse data-driven learning for effective and efficient biomedical image segmentation., Annual Review of Biomedical Engineering, Vol: 22, Pages: 127-153, ISSN: 1523-9829
Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation. We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 22 is June 4, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Chen C, Qin C, Qiu H, et al., 2020, Deep learning for cardiac image segmentation: A review, Frontiers in Cardiovascular Medicine, Vol: 7, Pages: 1-33, ISSN: 2297-055X
Deep learning has become the most widely used approach for cardiac imagesegmentation in recent years. In this paper, we provide a review of over 100cardiac image segmentation papers using deep learning, which covers commonimaging modalities including magnetic resonance imaging (MRI), computedtomography (CT), and ultrasound (US) and major anatomical structures ofinterest (ventricles, atria and vessels). In addition, a summary of publiclyavailable cardiac image datasets and code repositories are included to providea base for encouraging reproducible research. Finally, we discuss thechallenges and limitations with current deep learning-based approaches(scarcity of labels, model generalizability across different domains,interpretability) and suggest potential directions for future research.
Tarroni G, Bai W, Oktay O, et al., 2020, Large-scale quality control of cardiac imaging in population studies: application to UK Biobank, Scientific Reports, Vol: 10, ISSN: 2045-2322
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment isunfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) imagesto the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics(heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factorsincluding acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage(i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of thestacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slicemotion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastoliccardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involvedin UKBB CMR acquisition and for the ones who use the dataset for research purposes.
Chen C, Ouyang C, Tarroni G, et al., 2020, Unsupervised multi-modal style transfer for cardiac MR segmentation, MICCAI STACOM Workshop, Publisher: Springer International Publishing, Pages: 209-219, ISSN: 0302-9743
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.
Biffi C, Doumou G, Duan J, et al., 2020, Explainable anatomical shape analysis through deep hierarchical generative models., Publisher: arXiv
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.
Puyol-Antón E, Ruijsink B, Clough JR, et al., 2020, Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders, Pages: 22-30, ISSN: 0302-9743
© Springer Nature Switzerland AG 2020. Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function.
Lu P, Qiu H, Qin C, et al., 2020, Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features, Pages: 294-306, ISSN: 1865-0929
© 2020, Springer Nature Switzerland AG. This paper shows that deep modelling of subtle changes of cardiac motion can help in automated diagnosis of early onset of cardiac disease. In this paper, we model left ventricular (LV) cardiac motion in MRI sequences, based on a hybrid spatio-temporal network. Temporal data over long time periods is used as inputs to the model and delivers a dense displacement field (DDF) for regional analysis of LV function. A segmentation mask of the end-diastole (ED) frame is deformed by the predicted DDF from which regional analysis of LV function endocardial radius, thickness, circumferential strain (Ecc) and radial strain (Err) are estimated. Cardiac motion is estimated over MR cine loops. We compare the proposed technique to two other deep learning-based approaches and show that the proposed approach achieves promising predicted DDFs. Predicted DDFs are estimated on imaging data from healthy volunteers and patients with primary pulmonary hypertension from the UK Biobank. Experiments demonstrate that the proposed methods perform well in obtaining estimates of endocardial radii as cardiac motion-characteristic features for regional LV analysis.
Jokinen H, Koikkalainen J, Laakso HM, et al., 2020, Global Burden of Small Vessel Disease-Related Brain Changes on MRI Predicts Cognitive and Functional Decline, STROKE, Vol: 51, Pages: 170-178, ISSN: 0039-2499
Qiu H, Qin C, Le Folgoc L, et al., 2020, Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training, Pages: 186-194, ISSN: 0302-9743
© Springer Nature Switzerland AG 2020. Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.
Rachmadi MF, Valdes-Hernandez MDC, Li H, et al., 2020, Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images, COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, Vol: 79, ISSN: 0895-6111
Rueckert D, Schnabel JA, 2020, Model-Based and Data-Driven Strategies in Medical Image Computing, PROCEEDINGS OF THE IEEE, Vol: 108, Pages: 110-124, ISSN: 0018-9219
Meng Q, Zimmer V, Hou B, et al., 2019, Weakly supervised estimation of shadow confidence maps in fetal ultrasound imaging, IEEE Transactions on Medical Imaging, Vol: 38, Pages: 2755-2767, ISSN: 0278-0062
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. Additionally, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation and etc. to verify the effectiveness of our method. Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. We further demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion and automated biometric measurements.
Chen L, Lobotesis K, Rueckert D, et al., 2019, Timing an ischaemic stroke with just plain CT (and a little deep learning), Publisher: SAGE PUBLICATIONS LTD, Pages: 29-29, ISSN: 1747-4930
Chen L, Bentley P, Mori K, et al., 2019, Self-supervised learning for medical image analysis using image context restoration, Medical Image Analysis, Vol: 58, Pages: 1-12, ISSN: 1361-8415
Machine learning, particularly deep learning has boosted medical image analysis over the past years. Training a good model based on deep learning requires large amount of labelled data. However, it is often difficult to obtain a sufficient number of labelled images for training. In many scenarios the dataset in question consists of more unlabelled images than labelled ones. Therefore, boosting the performance of machine learning models by using unlabelled as well as labelled data is an important but challenging problem. Self-supervised learning presents one possible solution to this problem. However, existing self-supervised learning strategies applicable to medical images cannot result in significant performance improvement. Therefore, they often lead to only marginal improvements. In this paper, we propose a novel self-supervised learning strategy based on context restoration in order to better exploit unlabelled images. The context restoration strategy has three major features: 1) it learns semantic image features; 2) these image features are useful for different types of subsequent image analysis tasks; and 3) its implementation is simple. We validate the context restoration strategy in three common problems in medical imaging: classification, localization, and segmentation. For classification, we apply and test it to scan plane detection in fetal 2D ultrasound images; to localise abdominal organs in CT images; and to segment brain tumours in multi-modal MR images. In all three cases, self-supervised learning based on context restoration learns useful semantic features and lead to improved machine learning models for the above tasks.
Halliday BP, Balaban G, Costa CM, et al., 2019, Improving Arrhythmic Risk Stratification in Non-Ischemic Dilated Cardiomyopathy Through the Evaluation of Novel Scar Characteristics Using CMR, Scientific Sessions of the American-Heart-Association, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
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