165 results found
Winzeck S, Mocking SJT, Bezerra R, et al., 2019, Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI., AJNR Am J Neuroradiol, Vol: 40, Pages: 938-945
BACKGROUND AND PURPOSE: Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ensemble of convolutional neural networks trained on multiparametric DWI maps outperforms single networks trained on solo DWI parametric maps. MATERIALS AND METHODS: Convolutional neural networks were trained on combinations of DWI, ADC, and low b-value-weighted images from 116 subjects. The performances of the networks (measured by the Dice score, sensitivity, and precision) were compared with one another and with ensembles of 5 networks. To assess the generalizability of the approach, we applied the best-performing model to an independent Evaluation Cohort of 151 subjects. Agreement between manual and automated segmentations for identifying patients with large lesion volumes was calculated across multiple thresholds (21, 31, 51, and 70 cm3). RESULTS: An ensemble of convolutional neural networks trained on DWI, ADC, and low b-value-weighted images produced the most accurate acute infarct segmentation over individual networks (P < .001). Automated volumes correlated with manually measured volumes (Spearman ρ = 0.91, P < .001) for the independent cohort. For the task of identifying patients with large lesion volumes, agreement between manual outlines and automated outlines was high (Cohen κ, 0.86-0.90; P < .001). CONCLUSIONS: Acute infarcts are more accurately segmented using ensembles of convolutional neural networks trained with multiparametric maps than by using a single model trained with a solo map. Automated lesion segmentation has high agreement with manual techniques for identifying patients with large lesion volumes.
Walker I, Glocker B, 2019, Graph convolutional Gaussian processes, International Conference on Machine Learning (ICML), Publisher: PMLR, Pages: 6495-6504, ISSN: 2640-3498
We propose a novel Bayesian nonparametricmethod to learn translation-invariant relationshipson non-Euclidean domains. The resulting graphconvolutional Gaussian processes can be appliedto problems in machine learning for which theinput observations are functions with domains ongeneral graphs. The structure of these models al-lows for high dimensional inputs while retainingexpressibility, as is the case with convolutionalneural networks. We present applications of graphconvolutional Gaussian processes to images andtriangular meshes, demonstrating their versatilityand effectiveness, comparing favorably to existingmethods, despite being relatively simple models.
Folgoc LL, Castro DC, Tan J, et al., 2019, Controlling meshes via curvature: spin transformations for pose-invariant shape processing, International Conference on Information Processing in Medical Imaging (IPMI 2019), Publisher: Springer Verlag, Pages: 221-234, ISSN: 0302-9743
We investigate discrete spin transformations, a geometric framework tomanipulate surface meshes by controlling mean curvature. Applications includesurface fairing -- flowing a mesh onto say, a reference sphere -- and meshextrusion -- e.g., rebuilding a complex shape from a reference sphere andcurvature specification. Because they operate in curvature space, theseoperations can be conducted very stably across large deformations with no needfor remeshing. Spin transformations add to the algorithmic toolbox forpose-invariant shape analysis. Mathematically speaking, mean curvature is ashape invariant and in general fully characterizes closed shapes (together withthe metric). Computationally speaking, spin transformations make thatrelationship explicit. Our work expands on a discrete formulation of spintransformations. Like their smooth counterpart, discrete spin transformationsare naturally close to conformal (angle-preserving). This quasi-conformalitycan nevertheless be relaxed to satisfy the desired trade-off between areadistortion and angle preservation. We derive such constraints and propose aformulation in which they can be efficiently incorporated. The approach isshowcased on subcortical structures.
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, CLINICAL RADIOLOGY, Vol: 74, Pages: 346-356, ISSN: 0009-9260
Sokooti H, Saygili G, Glocker B, et al., 2019, Quantitative Error Prediction of Medical Image Registration using Regression Forests, Medical Image Analysis, ISSN: 1361-8415
Schlemper J, Oktay O, Schaap M, et al., 2019, Attention gated networks: Learning to leverage salient regions in medical images., Med Image Anal, Vol: 53, Pages: 197-207
We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks, including medical image classification and segmentation. For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Moreover, AGs guide the model activations to be focused around salient regions, which provides better insights into how model predictions are made. The source code for the proposed AG models is publicly available.
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
Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. An artificial RL agent learns to identify the optimal path to the landmark by interacting with an environment, in our case 3D images. Furthermore, we investigate the use of fixed- and multi-scale search strategies with novel hierarchical action steps in a coarse-to-fine manner. Several deep Q-network (DQN) architectures are evaluated for detecting multiple landmarks using three different medical imaging datasets: fetal head ultrasound (US), adult brain and cardiac magnetic resonance imaging (MRI). The performance of our agents surpasses state-of-the-art supervised and RL methods. Our experiments also show that multi-scale search strategies perform significantly better than fixed-scale agents in images with large field of view and noisy background such as in cardiac MRI. Moreover, the novel hierarchical steps can significantly speed up the searching process by a factor of 4-5 times.
Lee M, Petersen K, Pawlowski N, et al., 2019, TeTrIS: template transformer networks for image segmentation with shape priors, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
In this paper we introduce and compare different approaches for incorporating shape prior information into neural network based image segmentation. Specifically, we introduce the concept of template transformer networks where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors and is free of discretisation artefacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network based image segmentation.
Robinson R, Valindria VV, Bai W, et al., 2019, Automated quality control in image segmentation: application to the UK Biobank cardiac MR imaging study, Journal of Cardiovascular Magnetic Resonance, Vol: 21, ISSN: 1097-6647
Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools, e.g. image segmentation methods, are employed to derive quantitative measures or biomarkers for later analyses. Manual inspection and visual QC of each segmentation isn't feasible at large scale. However, it's important to be able to automatically detect when a segmentation method fails so as to avoid inclusion of wrong measurements into subsequent analyses which could lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4,800 cardiac magnetic resonance scans. We then apply our method to a large cohort of 7,250 cardiac MRI on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4,800 scans for which manual segmentations were available. We mimic real-world application of the method on 7,250 cardiac MRI where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that RCA has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.
Baweja C, Glocker B, Kamnitsas K, 2018, Towards continual learning in medical imaging, Medical imaging meets NIPS
This work investigates continual learning of two segmentation tasks in brain MRIwith neural networks. To explore in this context the capabilities of current methodsfor countering catastrophic forgetting of the first task when a new one is learned,we investigateelastic weight consolidation, a recently proposed method basedon Fisher information, originally evaluated on reinforcement learning of Atarigames. We use it to sequentially learn segmentation of normal brain structures andthen segmentation of white matter lesions. Our findings show this recent methodreduces catastrophic forgetting, while large room for improvement exists in thesechallenging settings for continual learning.
Mitchell J, Kamnitsas K, Singleton K, et al., 2018, DEEP LEARNING FOR ACCURATE, RAPID, FULLY AUTOMATIC MEASUREMENT OF BRAIN TUMOR-ASSOCIATED ABNORMALITY SEEN ON MRI, 23rd Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO) / 3rd CNS Anticancer Drug Discovery and Development Conference, Publisher: OXFORD UNIV PRESS INC, Pages: 180-181, ISSN: 1522-8517
Tarroni G, Oktay O, Bai W, et al., 2018, Learning-based quality control for cardiac MR images, IEEE Transactions on Medical Imaging, ISSN: 0278-0062
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.
Singleton K, Mitchell J, Ranjbar S, et al., 2018, DEEP LEARNING DETECTS DIFFERENCES IN THE MRIs OF MALE AND FEMALE GLIOMAS, 23rd Annual Scientific Meeting and Education Day of the Society-for-Neuro-Oncology (SNO) / 3rd CNS Anticancer Drug Discovery and Development Conference, Publisher: OXFORD UNIV PRESS INC, Pages: 177-177, ISSN: 1522-8517
Alansary A, Le Folgoc L, Vaillant G, et al., 2018, Automatic view planning with multi-scale deep reinforcement learning agents, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer Verlag, ISSN: 0302-9743
We propose a fully automatic method to find standardizedview planes in 3D image acquisitions. Standard view images are impor-tant in clinical practice as they provide a means to perform biometricmeasurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several DeepQ-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.
Hou B, Miolane N, Khanal B, et al., 2018, Computing CNN loss and gradients for pose estimation with Riemannian geometry, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer Verlag, ISSN: 0302-9743
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis. Deep learning methods often parameterise poses with a representation that separates rotation and translation.As commonly available frameworks do not provide means to calculate loss on a manifold, regression is usually performed using the L2-norm independently on the rotation’s and the translation’s parameterisations. This is a metric for linear spaces that does not take into account the Lie group structure of SE(3). In this paper, we propose a general Riemannian formulation of the pose estimation problem, and train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric. The loss between the ground truth and predicted pose (elements of the manifold) is calculated as the Riemannian geodesic distance, which couples together the translation and rotation components. Network weights are updated by back-propagating the gradient with respect to the predicted pose on the tangent space of the manifold SE(3). We thoroughly evaluate the effectiveness of our loss function by comparing its performance with popular and most commonly used existing methods, on tasks such as image-based localisation and intensity-based 2D/3D registration. We also show that hyper-parameters, used in our loss function to weight the contribution between rotations andtranslations, can be intrinsically calculated from the dataset to achievegreater performance margins.
Castro DC, Glocker B, 2018, Nonparametric density flows for MRI intensity normalisation, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: Springer, Cham, ISSN: 0302-9743
With the adoption of powerful machine learning methods in medical image analysis, it is becoming increasingly desirable to aggregate data that is acquired across multiple sites. However, the underlying assumption of many analysis techniques that corresponding tissues have consistent intensities in all images is often violated in multi-centre databases. We introduce a novel intensity normalisation scheme based on density matching, wherein the histograms are modelled as Dirichlet process Gaussian mixtures. The source mixture model is transformed to minimise its L2 divergence towards a target model, then the voxel intensities are transported through a mass-conserving flow to maintain agreement with the moving density. In a multi-centre study with brain MRI data, we show that the proposed technique produces excellent correspondence between the matched densities and histograms. We further demonstrate that our method makes tissue intensity statistics substantially more compatible between images than a baseline affine transformation and is comparable to state-of-the-art while providing considerably smoother transformations. Finally, we validate that nonlinear intensity normalisation is a step toward effective imaging data harmonisation.
Arslan S, Ktena SI, Glocker B, et al., 2018, Graph saliency maps through spectral convolutional networks: application to sex classification with brain connectivity, International Workshop on Graphs in Biomedical Image Analysis, Publisher: Springer Verlag, ISSN: 0302-9743
Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. Medical imaging and, in particular, neuroscience studies often rely on such graph representations, with brain connectivity networks being a characteristic example, while ultimately seeking the locus of phenotypic or disease-related differences in the brain. These regions of interest (ROIs) are, then, considered to be closely associated with function and/or behaviour. Driven by this, we explore GCNs for the task of ROI identification and propose a visual attribution method based on class activation mapping. By undertaking a sex classification task as proof of concept, we show that this method can be used to identify salient nodes (brain regions) without prior node labels. Based on experiments conducted on neuroimaging data of more than 5000 participants from UK Biobank, we demonstrate the robustness of the proposed method in highlighting reproducible regions across individuals. We further evaluate the neurobiological relevance of the identified regions based on evidence from large-scale UK Biobank studies.
Ferrante E, Oktay O, Glocker B, et al., 2018, On the adaptability of unsupervised CNN-based deformable image registration to unseen image domains, International Workshop on Machine Learning in Medical Imaging (MLMI), Publisher: Springer Verlag, Pages: 294-302, ISSN: 0302-9743
Deformable image registration is a fundamental problem in medical image analysis. During the last years, several methods based on deep convolutional neural networks (CNN) proved to be highly accurate to perform this task. These models achieved state-of-the-art accuracy while drastically reducing the required computational time, but mainly focusing on images of specific organs and modalities. To date, no work has reported on how these models adapt across different domains. In this work, we ask the question: can we use CNN-based registration models to spatially align images coming from a domain different than the one/s used at training time? We explore the adaptability of CNN-based image registration to different organs/modalities. We employ a fully convolutional architecture trained following an unsupervised approach. We consider a simple transfer learning strategy to study the generalisation of such model to unseen target domains, and devise a one-shot learning scheme taking advantage of the unsupervised nature of the proposed method. Evaluation on two publicly available datasets of X-Ray lung images and cardiac cine magnetic resonance sequences is provided. Our experiments suggest that models learned in different domains can be transferred at the expense of a decrease in performance, and that one-shot learning in the context of unsupervised CNN-based registration is a valid alternative to achieve consistent registration performance when only a pair of images from the target domain is available.
Valindria V, Lavdas I, Cerrolaza J, et al., 2018, Small organ segmentation in whole-body MRI using a two-stage FCN and weighting schemes, International Workshop on Machine Learning in Medical Imaging (MLMI) 2018, Publisher: Springer Verlag, Pages: 346-354, ISSN: 0302-9743
Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
Bai W, Sinclair M, Tarroni G, et al., 2018, Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Cardiovascular magnetic resonance (CMR) imaging is a standard imagingmodality for assessing cardiovascular diseases (CVDs), the leading cause ofdeath globally. CMR enables accurate quantification of the cardiac chambervolume, ejection fraction and myocardial mass, providing information fordiagnosis and monitoring of CVDs. However, for years, clinicians have beenrelying on manual approaches for CMR image analysis, which is time consumingand prone to subjective errors. It is a major clinical challenge toautomatically derive quantitative and clinically relevant information from CMRimages. Deep neural networks have shown a great potential in image patternrecognition and segmentation for a variety of tasks. Here we demonstrate anautomated analysis method for CMR images, which is based on a fullyconvolutional network (FCN). The network is trained and evaluated on alarge-scale dataset from the UK Biobank, consisting of 4,875 subjects with93,500 pixelwise annotated images. The performance of the method has beenevaluated using a number of technical metrics, including the Dice metric, meancontour distance and Hausdorff distance, as well as clinically relevantmeasures, including left ventricle (LV) end-diastolic volume (LVEDV) andend-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolicvolume (RVEDV) and end-systolic volume (RVESV). By combining FCN with alarge-scale annotated dataset, the proposed automated method achieves a highperformance on par with human experts in segmenting the LV and RV on short-axisCMR images and the left atrium (LA) and right atrium (RA) on long-axis CMRimages.
Robinson 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, ISSN: 0302-9743
Recent 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.
Hou B, Khanal B, Alansary A, et al., 2018, 3D reconstruction in canonical co-ordinate space from arbitrarily oriented 2D images, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 1737-1750, ISSN: 0278-0062
Limited capture range, and the requirement to provide high quality initialization for optimization-based 2D/3D image registration methods, can significantly degrade the performance of 3D image reconstruction and motion compensation pipelines. Challenging clinical imaging scenarios, which contain significant subject motion such as fetal in-utero imaging, complicate the 3D image and volume reconstruction process. In this paper we present a learning based image registration method capable of predicting 3D rigid transformations of arbitrarily oriented 2D image slices, with respect to a learned canonical atlas co-ordinate system. Only image slice intensity information is used to perform registration and canonical alignment, no spatial transform initialization is required. To find image transformations we utilize a Convolutional Neural Network (CNN) architecture to learn the regression function capable of mapping 2D image slices to a 3D canonical atlas space. We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline. Our learning based registration achieves an average spatial prediction error of 7 mm on simulated data and produces qualitatively improved reconstructions for heavily moving fetuses with gestational ages of approximately 20 weeks. Our model provides a general and computationally efficient solution to the 2D/3D registration initialization problem and is suitable for real-time scenarios.
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
Graphs are widely used as a natural framework that captures interactionsbetween individual elements represented as nodes in a graph. In medicalapplications, specifically, nodes can represent individuals within apotentially large population (patients or healthy controls) accompanied by aset of features, while the graph edges incorporate associations betweensubjects in an intuitive manner. This representation allows to incorporate thewealth of imaging and non-imaging information as well as individual subjectfeatures simultaneously in disease classification tasks. Previous graph-basedapproaches for supervised or unsupervised learning in the context of diseaseprediction solely focus on pairwise similarities between subjects, disregardingindividual characteristics and features, or rather rely on subject-specificimaging feature vectors and fail to model interactions between them. In thispaper, we present a thorough evaluation of a generic framework that leveragesboth imaging and non-imaging information and can be used for brain analysis inlarge populations. This framework exploits Graph Convolutional Networks (GCNs)and involves representing populations as a sparse graph, where its nodes areassociated with imaging-based feature vectors, while phenotypic information isintegrated as edge weights. The extensive evaluation explores the effect ofeach individual component of this framework on disease prediction performanceand further compares it to different baselines. The framework performance istested on two large datasets with diverse underlying data, ABIDE and ADNI, forthe prediction of Autism Spectrum Disorder and conversion to Alzheimer'sdisease, respectively. Our analysis shows that our novel framework can improveover state-of-the-art results on both databases, with 70.4% classificationaccuracy for ABIDE and 80.0% for ADNI.
Korkinof D, Rijken T, O'Neill M, et al., 2018, High-resolution mammogram synthesis using progressive generative adversarial networks
The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic, high-resolution medical images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the textural heterogeneity, fine structural details and specific tissue properties. In this paper, we explore the use of progressively trained generative adversarial networks (GANs) to synthesize mammograms, overcoming the underlying instabilities when training such adversarial models. This work is the first to show that generation of realistic synthetic medical images is feasible at up to 1280x1024 pixels, the highest resolution achieved for medical image synthesis, enabling visualizations within standard mammographic hanging protocols. We hope this work can serve as a useful guide and facilitate further research on GANs in the medical imaging domain.
Rajchl M, Pawlowski N, Rueckert D, et al., 2018, NeuroNet: fast and robust reproduction of multiple brain Image segmentation pipelines, International Conference on Medical Imaging with Deep Learning (MIDL), Publisher: MIDL
NeuroNet is a deep convolutional neural network mimicking multiple popularand state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.The network is trained on 5,000 T1-weighted brain MRI scans from the UK BiobankImaging Study that have been automatically segmented into brain tissue andcortical and sub-cortical structures using the standard neuroimaging pipelines.Training a single model from these complementary and partially overlappinglabel maps yields a new powerful "all-in-one", multi-output segmentation tool.The processing time for a single subject is reduced by an order of magnitudecompared to running each individual software package. We demonstrate very goodreproducibility of the original outputs while increasing robustness tovariations in the input data. We believe NeuroNet could be an important tool inlarge-scale population imaging studies and serve as a new standard inneuroscience by reducing the risk of introducing bias when choosing a specificsoftware package.
Valindria VV, Lavdas I, Bai W, et al., 2018, Domain adaptation for MRI organ segmentation using reverse classification accuracy, International Conference on Medical Imaging with Deep Learning (MIDL)
The variations in multi-center data in medical imaging studies have broughtthe necessity of domain adaptation. Despite the advancement of machine learningin automatic segmentation, performance often degrades when algorithms areapplied on new data acquired from different scanners or sequences than thetraining data. Manual annotation is costly and time consuming if it has to becarried out for every new target domain. In this work, we investigate automaticselection of suitable subjects to be annotated for supervised domain adaptationusing the concept of reverse classification accuracy (RCA). RCA predicts theperformance of a trained model on data from the new domain and differentstrategies of selecting subjects to be included in the adaptation via transferlearning are evaluated. We perform experiments on a two-center MR database forthe task of organ segmentation. We show that subject selection via RCA canreduce the burden of annotation of new data for the target domain.
Chen X, Pawlowski N, Rajchl M, et al., 2018, Deep generative models in the real-world: an open challenge from medical imaging
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributions in imaging data. These new models in medical image computing have important applications that form clinically relevant and very challenging unsupervised learning problems. In this paper, we explore the feasibility of using state-of-the-art auto-encoder-based deep generative models, such as variational and adversarial auto-encoders, for one such task: abnormality detection in medical imaging. We utilize typical, publicly available datasets with brain scans from healthy subjects and patients with stroke lesions and brain tumors. We use the data from healthy subjects to train different auto-encoder based models to learn the distribution of healthy images and detect pathologies as outliers. Models that can better learn the data distribution should be able to detect outliers more accurately. We evaluate the detection performance of deep generative models and compare them with non-deep learning based approaches to provide a benchmark of the current state of research. We conclude that abnormality detection is a challenging task for deep generative models and large room exists for improvement. In order to facilitate further research, we aim to provide carefully pre-processed imaging data available to the research community.
Schlemper J, Oktay O, Chen L, et al., 2018, Attention-Gated Networks for Improving Ultrasound Scan Plane Detection., International Conference on Medical Imaging with Deep Learning (MIDL)
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