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Conference paperBiffi C, Oktay O, Tarroni G, et al., 2018,
Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling
, International Conference On Medical Image Computing & Computer Assisted Intervention, Publisher: Springer, Pages: 464-471, ISSN: 0302-9743Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.
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Conference paperQin C, Bai W, Schlemper J, et al., 2018,
Joint motion estimation and segmentation from undersampled cardiac MR image
, Machine Learning for Medical Image Reconstruction Workshop, Pages: 55-63, ISSN: 0302-9743© 2018, Springer Nature Switzerland AG. Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures from the images, perfect reconstructions may not always be necessary as long as the images enable the means of extracting the clinically relevant measures. In this paper, we work on jointly predicting cardiac motion estimation and segmentation directly from undersampled data, which are two important steps in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In particular, a unified model consisting of both motion estimation branch and segmentation branch is learned by optimising the two tasks simultaneously. Additional corresponding fully-sampled images are incorporated into the network as a parallel sub-network to enhance and guide the learning during the training process. Experimental results using cardiac MR images from 220 subjects show that the proposed model is robust to undersampled data and is capable of predicting results that are close to that from fully-sampled ones, while bypassing the usual image reconstruction stage.
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Conference paperQin C, Bai W, Schlemper J, et al., 2018,
Joint learning of motion estimation and segmentation for cardiac MR image sequences
, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: Springer Verlag, Pages: 472-480Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MlRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed.
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Conference paperBai W, Suzuki H, Qin C, et al., 2018,
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), ISSN: 0302-9743Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct application of a segmentation algorithm to each time frame of a sequence may ignore the temporal continuity inherent in the sequence. In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task. A key challenge in training this network is that the available manual annotations are temporally sparse, which forbids end-to-end training. We address this challenge by performing non-rigid label propagation on the annotations and introducing an exponentially weighted loss function for training. Experiments on aortic MR image sequences demonstrate that the proposed method significantly improves both accuracy and temporal smoothness of segmentation, compared to a baseline method that utilises spatial information only. It achieves an average Dice metric of 0.960 for the ascending aorta and 0.953 for the descending aorta.
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Conference paperSchlemper J, Castro DC, Bai W, et al., 2018,
Bayesian deep learning for accelerated MR image reconstruction
, International Workshop on Machine Learning for Medical Image Reconstruction, Publisher: Springer, Cham, Pages: 64-71, ISSN: 0302-9743Recently, many deep learning (DL) based MR image reconstruction methods have been proposed with promising results. However, only a handful of work has been focussing on characterising the behaviour of deep networks, such as investigating when the networks may fail to reconstruct. In this work, we explore the applicability of Bayesian DL techniques to model the uncertainty associated with DL-based reconstructions. In particular, we apply MC-dropout and heteroscedastic loss to the reconstruction networks to model epistemic and aleatoric uncertainty. We show that the proposed Bayesian methods achieve competitive performance when the test images are relatively far from the training data distribution and outperforms when the baseline method is over-parametrised. In addition, we qualitatively show that there seems to be a correlation between the magnitude of the produced uncertainty maps and the error maps, demonstrating the potential utility of the Bayesian DL methods for assessing the reliability of the reconstructed images.
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Conference paperRobinson R, Oktay O, Bai W, et al., 2018,
Real-time prediction of segmentation quality
, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer Verlag, Pages: 578-585, ISSN: 0302-9743Recent advances in deep learning based image segmentationmethods have enabled real-time performance with human-level accuracy.However, occasionally even the best method fails due to low image qual-ity, artifacts or unexpected behaviour of black box algorithms. Beingable to predict segmentation quality in the absence of ground truth is ofparamount importance in clinical practice, but also in large-scale studiesto avoid the inclusion of invalid data in subsequent analysis.In this work, we propose two approaches of real-time automated qualitycontrol for cardiovascular MR segmentations using deep learning. First,we train a neural network on 12,880 samples to predict Dice SimilarityCoefficients (DSC) on a per-case basis. We report a mean average error(MAE) of 0.03 on 1,610 test samples and 97% binary classification accu-racy for separating low and high quality segmentations. Secondly, in thescenario where no manually annotated data is available, we train a net-work to predict DSC scores from estimated quality obtained via a reversetesting strategy. We report an MAE = 0.14 and 91% binary classifica-tion accuracy for this case. Predictions are obtained in real-time which,when combined with real-time segmentation methods, enables instantfeedback on whether an acquired scan is analysable while the patient isstill in the scanner. This further enables new applications of optimisingimage acquisition towards best possible analysis results.
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Conference paperDuan J, Schlemper J, Bai W, et al., 2018,
Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension
, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Pages: 595-603, ISSN: 0302-9743 -
Journal articleTimmermann C, Timmermann Slater C, Roseman L, et al., 2018,
DMT models the near-death experience
, Frontiers in Psychology, Vol: 9, ISSN: 1664-1078Near-death experiences (NDEs) are complex subjective experiences, which have been previously associated with the psychedelic experience and more specifically with the experience induced by the potent serotonergic, N,N-Dimethyltryptamine (DMT). Potential similarities between both subjective states have been noted previously, including the subjective feeling of transcending one’s body and entering an alternative realm, perceiving and communicating with sentient ‘entities’ and themes related to death and dying. In this within-subjects placebo-controled study we aimed to test the similarities between the DMT state and NDEs, by administering DMT and placebo to 13 healthy participants, who then completed a validated and widely used measure of NDEs. Results revealed significant increases in phenomenological features associated with the NDE, following DMT administration compared to placebo. Also, we found significant relationships between the NDE scores and DMT-induced ego-dissolution and mystical-type experiences, as well as a significant association between NDE scores and baseline trait ‘absorption’ and delusional ideation measured at baseline. Furthermore, we found a significant overlap in nearly all of the NDE phenomenological features when comparing DMT-induced NDEs with a matched group of ‘actual’ NDE experiencers. These results reveal a striking similarity between these states that warrants further investigation.
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Journal articleUnderwood J, Cole JH, Leech R, et al., 2018,
Multivariate pattern analysis of volumetric neuroimaging data and its relationship with cognitive function in treated HIV-disease
, Journal of Acquired Immune Deficiency Syndromes, Vol: 78, Pages: 429-436, ISSN: 1525-4135BACKGROUND: Accurate prediction of longitudinal changes in cognitive function would potentially allow targeted intervention in those at greatest risk of cognitive decline. We sought to build a multivariate model using volumetric neuroimaging data alone to accurately predict cognitive function. METHODS: Volumetric T1-weighted neuroimaging data from virally suppressed HIV-positive individuals from the CHARTER cohort (n=139) were segmented into grey and white matter and spatially normalised before were entering into machine learning models. Prediction of cognitive function at baseline and longitudinally was determined using leave-one-out cross validation. Additionally, a multivariate model of brain ageing was used to measure the deviation of apparent brain age from chronological age and assess its relationship with cognitive function. RESULTS: Cognitive impairment, defined using the global deficit score, was present in 37.4%. However, it was generally mild and occurred more commonly in those with confounding comorbidities (p<0.001). Although multivariate prediction of cognitive impairment as a dichotomous variable at baseline was poor (AUC 0.59), prediction of the global T-score was better than a comparable linear model (adjusted R=0.08, p<0.01 vs. adjusted R=0.01, p=0.14). Accurate prediction of longitudinal changes in cognitive function was not possible (p=0.82).Brain-predicted age exceeded chronological age by mean (95% confidence interval) 1.17 (-0.14-2.53) years, but was greatest in those with confounding comorbidities (5.87 [1.74-9.99] years) and prior AIDS (3.03 [0.00-6.06] years). CONCLUSION: Accurate prediction of cognitive impairment using multivariate models using only T1-weighted data was not achievable, which may reflect the small sample size, heterogeneity of the data or that impairment was usually mild.
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Journal articleDeb S, Leeson V, Aimola L, et al., 2018,
Aggression following traumatic brain injury: effectiveness of Risperidone (AFTER): study protocol for a feasibility randomised controlled trial
, Trials, Vol: 19, ISSN: 1745-6215BackgroundTraumatic brain injury (TBI) is a major public health concern and many people develop long-lasting physical and neuropsychiatric consequences following a TBI. Despite the emphasis on physical rehabilitation, it is the emotional and behavioural consequences that have greater impact on people with TBI and their families. One such problem behaviour is aggression which can be directed towards others, towards property or towards the self. Aggression is reported to be common after TBI (37–71%) and causes major stress for patients and their families. Both drug and non-drug interventions are used to manage this challenging behaviour, but the evidence-base for these interventions is poor and no drugs are currently licensed for the treatment of aggression following TBI. The most commonly used drugs for this purpose are antipsychotics, particularly second-generation drugs such as risperidone. Despite this widespread use, randomised controlled trials (RCTs) of antipsychotic drugs, including risperidone, have not been conducted. We have, therefore, set out to test the feasibility of conducting an RCT of this drug for people who have aggressive behaviour following TBI.Methods/designWe will examine the feasibility of conducting a placebo-controlled, double-blind RCT of risperidone for the management of aggression in adults with TBI and also assess participants’ views about their experience of taking part in the study.We will randomise 50 TBI patients from secondary care services in four centres in London and Kent to up to 4 mg of risperidone orally or an inert placebo and follow them up 12 weeks later. Participants will be randomised to active or control treatment in a 1:1 ratio via an external and remote web-based randomisation service. Participants will be assessed at baseline and 12-week follow-up using a battery of assessment scales to measure changes in aggressive behaviour (MOAS, IRQ) as well as global functioning (GOS-E, CGI), quality of life (EQ-5D-5L
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