731 results found
Suinesiaputra A, Ablin P, Alba X, et al., 2018, Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 22, Pages: 503-515, ISSN: 2168-2194
Kamnitsas K, Bai W, Ferrante E, et al., 2018, Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation, MICCAI BrainLes Workshop
Makropoulos A, Robinson EC, Schuh A, et al., 2018, The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction., NeuroImage, Vol: 173, Pages: 88-112, ISSN: 1053-8119
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
Knoll F, Maier A, Rueckert D, 2018, Preface, ISBN: 9783030001285
Oksuz I, Clough J, Bustin A, et al., 2018, Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction, 1st Workshop on Machine Learning for Medical Image Reconstruction (MLMIR) held as part of the 21st Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 21-29, ISSN: 0302-9743
Wright R, Khanal B, Gomez A, et al., 2018, LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images, 1st International Workshop on Data Driven Treatment Response Assessment (DATRA) / 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis (PIPPI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 149-159, ISSN: 0302-9743
Oksuz I, Ruijsink B, Puyol-Anton E, et al., 2018, Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 250-258, ISSN: 0302-9743
Sinclair M, Peressutti D, Puyol-Anton E, et al., 2018, Myocardial strain computed at multiple spatial scales from tagged magnetic resonance imaging: Estimating cardiac biomarkers for CRT patients, MEDICAL IMAGE ANALYSIS, Vol: 43, Pages: 169-185, ISSN: 1361-8415
Khanal B, Gomez A, Toussaint N, et al., 2018, EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG
Cajanus A, Hall A, Koikkalainen J, et al., 2018, Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis, DEMENTIA AND GERIATRIC COGNITIVE DISORDERS EXTRA, Vol: 8, Pages: 51-59, ISSN: 1664-5464
Kuklisova-Murgasova M, Estrin GL, Nunes RG, et al., 2018, Distortion Correction in Fetal EPI Using Non-Rigid Registration With a Laplacian Constraint, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 37, Pages: 12-19, ISSN: 0278-0062
Balaban G, Halliday BP, Costa CM, et al., 2018, The Effects of Non-ischemic Fibrosis Texture and Density on Mechanisms of Reentry, 45th Computing in Cardiology Conference (CinC), Publisher: IEEE, ISSN: 2325-8861
Ktena SI, Parisot S, Ferrante E, et al., 2017, Metric learning with spectral graph convolutions on brain connectivity networks., NeuroImage, Vol: 169, Pages: 431-442, ISSN: 1053-8119
Graph representations are often used to model structured data at an individual or population level and have numerous applications in pattern recognition problems. In the field of neuroscience, where such representations are commonly used to model structural or functional connectivity between a set of brain regions, graphs have proven to be of great importance. This is mainly due to the capability of revealing patterns related to brain development and disease, which were previously unknown. Evaluating similarity between these brain connectivity networks in a manner that accounts for the graph structure and is tailored for a particular application is, however, non-trivial. Most existing methods fail to accommodate the graph structure, discarding information that could be beneficial for further classification or regression analyses based on these similarities. We propose to learn a graph similarity metric using a siamese graph convolutional neural network (s-GCN) in a supervised setting. The proposed framework takes into consideration the graph structure for the evaluation of similarity between a pair of graphs, by employing spectral graph convolutions that allow the generalisation of traditional convolutions to irregular graphs and operates in the graph spectral domain. We apply the proposed model on two datasets: the challenging ABIDE database, which comprises functional MRI data of 403 patients with autism spectrum disorder (ASD) and 468 healthy controls aggregated from multiple acquisition sites, and a set of 2500 subjects from UK Biobank. We demonstrate the performance of the method for the tasks of classification between matching and non-matching graphs, as well as individual subject classification and manifold learning, showing that it leads to significantly improved results compared to traditional methods.
Guerrero R, Qin C, Oktay O, et al., 2017, White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks, NeuroImage: Clinical, Vol: 17, Pages: 918-934, ISSN: 2213-1582
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, is comprised of an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to
Maas AIR, Menon DK, Adelson PD, et al., 2017, Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research, The Lancet Neurology, Vol: 16, Pages: 987-1048, ISSN: 1474-4422
Ledig C, Kamnitsas K, Koikkalainen J, et al., 2017, Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging., PLoS ONE, Vol: 12, ISSN: 1932-6203
Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).
Pawlowski N, Ktena SI, Lee MCH, et al., 2017, DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
We present DLTK, a toolkit providing baseline implementations for efficientexperimentation with deep learning methods on biomedical images. It builds ontop of TensorFlow and its high modularity and easy-to-use examples allow for alow-threshold access to state-of-the-art implementations for typical medicalimaging problems. A comparison of DLTK's reference implementations of popularnetwork architectures for image segmentation demonstrates new top performanceon the publicly available challenge data "Multi-Atlas Labeling Beyond theCranial Vault". The average test Dice similarity coefficient of $81.5$ exceedsthe previously best performing CNN ($75.7$) and the accuracy of the challengewinning method ($79.0$).
Kelly CJ, Makropoulos A, Cordero-Grande L, et al., 2017, Impaired development of the cerebral cortex in infants with congenital heart disease is correlated to reduced cerebral oxygen delivery, SCIENTIFIC REPORTS, Vol: 7, ISSN: 2045-2322
Robinson EC, Garcia K, Glasser MF, et al., 2017, Multimodal surface matching with higher-order smoothness constraints., NeuroImage, Vol: 167, Pages: 453-465, ISSN: 1053-8119
In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies; and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of populatio
Schlemper J, Caballero J, Hajnal J, et al., 2017, A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 491-503, ISSN: 0278-0062
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling. Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Secondly, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10s and, for the 2D case, each image frame can be reconstructed in 23ms, enabling real-time applications.
Sinclair M, Bai W, Puyol-Antón E, et al., 2017, Fully automated segmentation-based respiratory motion correction of multiplanar cardiac magnetic resonance images for large-scale datasets, International Conference On Medical Image Computing & Computer Assisted Intervention, Pages: 332-340, ISSN: 0302-9743
© Springer International Publishing AG 2017. Cardiac magnetic resonance (CMR) can be used for quantitative analysis of heart function. However, CMR imaging typically involves acquiring 2D image planes during separate breath-holds, often resulting in misalignment of the heart between image planes in 3D. Accurate quantitative analysis requires a robust 3D reconstruction of the heart from CMR images, which is adversely affected by such motion artifacts. Therefore, we propose a fully automated method for motion correction of CMR planes using segmentations produced by fully convolutional neural networks (FCNs). FCNs are trained on 100 UK Biobank subjects to produce short-axis and long-axis segmentations, which are subsequently used in an iterative registration algorithm for correcting breath-hold induced motion artifacts. We demonstrate significant improvements in motion-correction over image-based registration, with strong correspondence to results obtained using manual segmentations. We also deploy our automatic method on 9,353 subjects in the UK Biobank database, demonstrating significant improvements in 3D plane alignment.
Oktay O, Ferrante E, Kamnitsas K, et al., 2017, Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 384-395, ISSN: 0278-0062
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.
Cerrolaza JJ, Oktay O, Gomez A, et al., 2017, Fetal skull segmentation in 3D ultrasound via structured geodesic random forest, Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Pages: 25-32, ISSN: 0302-9743
© Springer International Publishing AG 2017. Ultrasound is the primary imaging method for prenatal screening and diagnosis of fetal anomalies. Thanks to its non-invasive and non-ionizing properties, ultrasound allows quick, safe and detailed evaluation of the unborn baby, including the estimation of the gestational age, brain and cranium development. However, the accuracy of traditional 2D fetal biometrics is dependent on operator expertise and subjectivity in 2D plane finding and manual marking. 3D ultrasound has the potential to reduce the operator dependence. In this paper, we propose a new random forest-based segmentation framework for fetal 3D ultrasound volumes, able to efficiently integrate semantic and structural information in the classification process. We introduce a new semantic features space able to encode spatial context via generalized geodesic distance transform. Unlike alternative auto-context approaches, this new set of features is efficiently integrated into the same forest using contextual trees. Finally, we use a new structured labels space as alternative to the traditional atomic class labels, able to capture morphological variability of the target organ. Here, we show the potential of this new general framework segmenting the skull in 3D fetal ultrasound volumes, significantly outperforming alternative random forest-based approaches.
Bowles, Qin C, Guerrero R, et al., 2017, Brain Lesion Segmentation through Image Synthesis and Outlier Detection, NeuroImage: Clinical, Vol: 16, Pages: 643-658, ISSN: 2213-1582
Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.
Kanavati F, Misawa K, Fujiwara M, et al., 2017, Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation, International Workshop on Machine Learning in Medical Imaging (MLMI)
Garcia KE, Robinson EC, Alexopoulos D, et al., 2017, Dynamic patterns of cortical expansion during folding of the preterm human brain, Publisher: Cold Spring Harbor Laboratory
<jats:p>During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggests that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a new method to estimate local growth from sequential cortical reconstructions. Using anatomically-constrained Multimodal Surface Matching (aMSM), we obtain accurate, physically-guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned 2-4 times during the period of rapid cortical expansion (28 to 38 weeks postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with patterns of active folding. Furthermore, these growth patterns shift over the course of development, with non-injured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully-automated surface analysis, based on tools freely available to the brain mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.</jats:p><jats:sec><jats:title>Significance Statement</jats:title><jats:p>The human brain exhibits complex folding patterns that emerge during the third trimester of fetal development. Minor folds are quasi-ra
Hou B, Alansary A, McDonagh S, et al., 2017, Predicting slice-to-volume transformation in presence of arbitrary subject motion, 20th International Conference on Medical Image Computing and Computer Assisted Intervention 2017, Publisher: Springer, ISSN: 0302-9743
This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range andneed for very good initialization of state-of-the-art image registrationmethods. We propose a regression approach that learns to predict rota-tion and translations of arbitrary 2D image slices from 3D volumes, withrespect to a learned canonical atlas co-ordinate system. To this end,we utilize Convolutional Neural Networks (CNNs) to learn the highlycomplex regression function that maps 2D image slices into their cor-rect position and orientation in 3D space. Our approach is attractivein challenging imaging scenarios, where significant subject motion com-plicates reconstruction performance of 3D volumes from 2D slice data.We extensively evaluate the effectiveness of our approach quantitativelyon simulated MRI brain data with extreme random motion. We furtherdemonstrate qualitative results on fetal MRI where our method is in-tegrated into a full reconstruction and motion compensation pipeline.With our CNN regression approach we obtain an average prediction er-ror of 7mm on simulated data, and convincing reconstruction quality ofimages of very young fetuses where previous methods fail. We furtherdiscuss applications to Computed Tomography (CT) and X-Ray pro-jections. Our approach is a general solution to the 2D/3D initializationproblem. It is computationally efficient, with prediction times per sliceof a few milliseconds, making it suitable for real-time scenarios.
Parisot S, Glocker B, Ktena SI, et al., 2017, A flexible graphical model for multi-modal parcellation of the cortex., NeuroImage, Vol: 162, Pages: 226-248, ISSN: 1053-8119
Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.
Cnossen MC, Huijben JA, van der Jagt M, et al., 2017, Variation in monitoring and treatment policies for intracranial hypertension in traumatic brain injury: A survey in 66 neurotrauma centers participating in the CENTER-TBI study, Critical Care, Vol: 21, ISSN: 1364-8535
Background: No definitive evidence exists on how intracranial hypertension should be treated in patients with traumatic brain injury (TBI). It is therefore likely that centers and practitioners individually balance potential benefits and risks of different intracranial pressure (ICP) management strategies, resulting in practice variation. The aim of this study was to examine variation in monitoring and treatment policies for intracranial hypertension in patients with TBI. Methods: A 29-item survey on ICP monitoring and treatment was developed on the basis of literature and expert opinion, and it was pilot-tested in 16 centers. The questionnaire was sent to 68 neurotrauma centers participating in the Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Results: The survey was completed by 66 centers (97% response rate). Centers were mainly academic hospitals (n=60, 91%) and designated level I trauma centers (n=44, 67%). The Brain Trauma Foundation guidelines were used in 49 (74%) centers. Approximately 90% of the participants (n=58) indicated placing an ICP monitor in patients with severe TBI and computed tomographic abnormalities. There was no consensus on other indications or on peri-insertion precautions. We found wide variation in the use of first- and second-tier treatments for elevated ICP. Approximately half of the centers were classified as using a relatively aggressive approach to ICP monitoring and treatment (n=32, 48%), whereas the others were considered more conservative (n=34, 52%). Conclusions: Substantial variation was found regarding monitoring and treatment policies in patients with TBI and intracranial hypertension. The results of this survey indicate a lack of consensus between European neurotrauma centers and provide an opportunity and necessity for comparative effectiveness research.
Robinson R, Valindria V, Bai W, et al., 2017, Automatic quality control of cardiac MRI segmentation in large-scale population imaging, Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, Publisher: Springer, Pages: 720-727, ISSN: 0302-9743
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 such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. 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 cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study.
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