740 results found
Arslan S, Ktena SI, Makropoulos A, et al., 2017, Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex, Neuroimage, Vol: 170, Pages: 5-30, ISSN: 1095-9572
The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
Kamnitsas K, Ferrante E, Parisot S, et al., 2017, DeepMedic for brain tumor segmentation, Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Publisher: Springer, Pages: 138-149, ISSN: 0302-9743
Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic , a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.
Giannakidis A, Oktay O, Keegan J, et al., 2017, Super-resolution Reconstruction of Late Gadolinium Cardiovascular Magnetic Resonance Images using a Residual Convolutional Neural Network, The 25th Scientific Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2017)
Karasawa K, Oda M, Kitasaka T, et al., 2017, Multi-atlas pancreas segmentation: Atlas selection based on vessel structure, Medical Image Analysis, Vol: 39, Pages: 18-28, ISSN: 1361-8415
Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.
Ciller C, De Zanet S, Kamnitsas K, et al., 2017, Multi-channel MRI segmentation of eye structures and tumors using patient-specific features, PLOS ONE, Vol: 12, ISSN: 1932-6203
Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.
Passerat-Palmbach J, Reuillon RR, Leclaire ML, et al., 2017, Reproducible large-scale neuroimaging studies with the OpenMOLE workflow management system, Frontiers in Neuroinformatics, Vol: 11, ISSN: 1662-5196
OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution.Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments.OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition.The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments.Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behaviour of an application, or perform automatic parameter tuning.In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerisation solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously.The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstrati
Sinclair M, Peressutti D, Puyol-Anton E, et al., 2017, Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas, 7th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 57-65, ISSN: 0302-9743
Schlemper J, Caballero J, Hajnal JV, et al., 2017, A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction, 25th Biennial International Conference on Information Processing in Medical Imaging (IPMI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 647-658, ISSN: 0302-9743
Baumgartner CF, Oktay O, Rueckert D, 2017, Fully convolutional networks in medical imaging: Applications to image enhancement and recognition, Advances in Computer Vision and Pattern Recognition, Pages: 159-179
© Springer International Publishing Switzerland 2017. Convolutional neural networks (CNNs) are hierarchical models that have immense representational capacity and have been successfully applied to computer vision problems including object localisation, classification and super-resolution. A particular example of CN Nmodels, knownas fully convolutional network (FCN), has been shown to offer improved computational efficiency and representation learning capabilities due to simplermodel parametrisation and spatial consistency of extracted features. In this chapter, we demonstrate the power and applicability of this particular model on two medical imaging tasks, image enhancement via super-resolution and image recognition. In both examples, experimental results show that FCN models can significantly outperform traditional learning-based approaches while achieving real-time performance. Additionally, we demonstrate that the proposed image classification FCN model can be used in organ localisation task as well without requiring additional training data.
Oda M, Shimizu N, Roth HR, et al., 2017, 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation, 3rd MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 7th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 222-230, ISSN: 0302-9743
Robinson E, Glocker B, Rajchl M, et al., 2016, Discrete Optimisation for Group-wise Cortical Surface Atlasing, International Workshop on Biomedical Image Registration, Publisher: IEEE, ISSN: 2160-7516
This paper presents a novel method for cortical surfaceatlasing. Group-wise registration is performed through adiscrete optimisation framework that seeks to simultaneouslyimprove pairwise correspondences between surfacefeature sets, whilst minimising a global cost relating to therank of the feature matrix. It is assumed that when fullyaligned, features will be highly linearly correlated, andthus have low rank. The framework is regularised throughuse of multi-resolution control point grids and higher-ordersmoothness terms, calculated by considering deformationstrain for displacements of triplets of points. Accordinglythe discrete framework is solved through high-order cliquereduction. The framework is tested on cortical foldingbased alignment, using data from the Human ConnectomeProject. Preliminary results indicate that group-wise alignmentimproves folding correspondences, relative to registrationbetween all pair-wise combinations, and registrationto a global average template.
Gomez A, Oktay O, Rueckert D, et al., 2016, Regional differences in end-diastolic volumes between 3D echo and CMR in HLHS patients, Frontiers in Pediatrics, Vol: 4, ISSN: 2296-2360
Ultrasound is commonly thought to underestimate ventricular volumes compared to magnetic resonance imaging (MRI), although the reason for this and the spatial distribution of the volume difference is not well understood. In this paper, we use landmark-based image registration to spatially align MRI and ultrasound images from patients with hypoplastic left heart syndrome and carry out a qualitative and quantitative spatial comparison of manual segmentations of the ventricular volume obtained from the respective modalities. In our experiments, we have found a trend showing volumes estimated from ultrasound to be smaller than those obtained from MRI (by approximately up to 20 ml), and that important contributors to this difference are the presence of artifacts such as shadows in the echo images and the different criteria to include or exclude image features as part of the ventricular volume.
Xie Z, 2016, Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images
Schafer S, de Marvao A, Adami E, et al., 2016, Titin truncating variants affect heart function in disease cohorts and the general population, Nature Genetics, Vol: 49, Pages: 46-53, ISSN: 1546-1718
Titin-truncating variants (TTNtv) commonly cause dilated cardiomyopathy (DCM). TTNtv are also encountered in ~1% of the general population, where they may be silent, perhaps reflecting allelic factors. To better understand TTNtv, we integrated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with disparate TTNtv. In patients with DCM, TTNtv throughout titin were significantly associated with DCM. Ribosomal profiling in rat showed the translational footprint of premature stop codons in Ttn, TTNtv-position-independent nonsense-mediated degradation of the mutant allele and a signature of perturbed cardiac metabolism. Heart physiology in rats with TTNtv was unremarkable at baseline but became impaired during cardiac stress. In healthy humans, machine-learning-based analysis of high-resolution cardiac imaging showed TTNtv to be associated with eccentric cardiac remodeling. These data show that TTNtv have molecular and physiological effects on the heart across species, with a continuum of expressivity in health and disease.
Rajchl M, Lee MCH, Oktay O, et al., 2016, DeepCut: object segmentation from bounding box annotations using convolutional neural networks, IEEE Transactions on Medical Imaging, Vol: 36, Pages: 674-683, ISSN: 0278-0062
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
Kamnitsas K, Ledig C, Newcombe VFJ, et al., 2016, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, Vol: 36, Pages: 61-78, ISSN: 1361-8423
We propose a dual pathway, 11-layers deep, three-dimensional ConvolutionalNeural Network for the challenging task of brain lesion segmentation. Thedevised architecture is the result of an in-depth analysis of the limitations ofcurrent networks proposed for similar applications. To overcome the computationalburden of processing 3D medical scans, we have devised an efficientand effective dense training scheme which joins the processing of adjacentimage patches into one pass through the network while automatically adaptingto the inherent class imbalance present in the data. Further, we analyzethe development of deeper, thus more discriminative 3D CNNs. In order toincorporate both local and larger contextual information, we employ a dualpathway architecture that processes the input images at multiple scales simultaneously.For post-processing of the network’s soft segmentation, we use a3D fully connected Conditional Random Field which effectively removes falsepositives. Our pipeline is extensively evaluated on three challenging tasks oflesion segmentation in multi-channel MRI patient data with traumatic braininjuries, brain tumors, and ischemic stroke. We improve on the state-of-theartfor all three applications, with top ranking performance on the publicbenchmarks BRATS 2015 and ISLES 2015. Our method is computationallyefficient, which allows its adoption in a variety of research and clinicalsettings. The source code of our implementation is made publicly available
Ktena SI, Parisot S, Passerat-Palmbach J, et al., 2016, Comparison of brain networks with unknown correspondences, MICCAI Workshop on Brain Analysis using COnnectivity Networks (BACON) 2016, Publisher: BACON 2016
Graph theory has drawn a lot of attention in the field of Neuroscience duringthe last decade, mainly due to the abundance of tools that it provides toexplore the interactions of elements in a complex network like the brain. Thelocal and global organization of a brain network can shed light on mechanismsof complex cognitive functions, while disruptions within the network can belinked to neurodevelopmental disorders. In this effort, the construction of arepresentative brain network for each individual is critical for furtheranalysis. Additionally, graph comparison is an essential step for inference andclassification analyses on brain graphs. In this work we explore a method basedon graph edit distance for evaluating graph similarity, when correspondencesbetween network elements are unknown due to different underlying subdivisionsof the brain. We test this method on 30 unrelated subjects as well as 40 twinpairs and show that this method can accurately reflect the higher similaritybetween two related networks compared to unrelated ones, while identifying nodecorrespondences.
Peressutti D, Sinclair M, Bai W, et al., 2016, A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction, MEDICAL IMAGE ANALYSIS, Vol: 35, Pages: 669-684, ISSN: 1361-8415
Tong, gray, gao, et al., 2017, Multi-modal classification of Alzheimer's disease using nonlinear graph fusion, Pattern Recognition, ISSN: 1873-5142
Baumgartner CH, Kamnitsas K, Matthew J, et al., 2016, Real-time standard scan plane detection and localisation in fetal ultrasound using fully convolutional neural networks, International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 206, Publisher: Springer, Pages: 203-211
Fetal mid-pregnancy scans are typically carried out accordingto fixed protocols. Accurate detection of abnormalities and correctbiometric measurements hinge on the correct acquisition of clearlydefined standard scan planes. Locating these standard planes requires ahigh level of expertise. However, there is a worldwide shortage of expertsonographers. In this paper, we consider a fully automated system basedon convolutional neural networks which can detect twelve standard scanplanes as defined by the UK fetal abnormality screening programme. Thenetwork design allows real-time inference and can be naturally extendedto provide an approximate localisation of the fetal anatomy in the image.Such a framework can be used to automate or assist with scan planeselection, or for the retrospective retrieval of scan planes from recordedvideos. The method is evaluated on a large database of 1003 volunteermid-pregnancy scans. We show that standard planes acquired in a clinicalscenario are robustly detected with a precision and recall of 69 %and 80 %, which is superior to the current state-of-the-art. Furthermore,we show that it can retrospectively retrieve correct scan planes with anaccuracy of 71 % for cardiac views and 81 % for non-cardiac views.
Parisot S, Glocker B, Schirmer MD, et al., 2016, GraMPa: Graph-based Multi-modal Parcellation of the Cortex using Fusion Moves, 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Publisher: Springer Verlag, ISSN: 0302-9743
Glocker B, Konukoglu E, Lavdas I, et al., 2016, Correction of Fat-Water Swaps in Dixon MRI, 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Publisher: Springer Verlag, ISSN: 0302-9743
The Dixon method is a popular and widely used technique for fat-water separation in magnetic resonance imaging, and today, nearly all scanner manufacturers are offering a Dixon-type pulse sequence that produces scans with four types of images: in-phase, out-of-phase, fat-only, and water-only. A natural ambiguity due to phase wrapping and local minima in the optimization problem cause a frequent artifact of fat-water inversion where fat- and water-only voxel values are swapped. This artifact affects up to 10 % of routinely acquired Dixon images, and thus, has severe impact on subsequent analysis. We propose a simple yet very effective method, Dixon-Fix, for correcting fat-water swaps. Our method is based on regressing fat- and water-only images from in- and out-of-phase images by learning the conditional distribution of image appearance. The predicted images define the unary potentials in a globally optimal maximum-a-posteriori estimation of the swap labeling with spatial consistency. We demonstrate the effectiveness of our approach on whole-body MRI with various types of fat-water swaps.
Alansary A, Kamnitsas K, Davidson A, et al., 2016, Fast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI, 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Publisher: Springer Verlag, ISSN: 0302-9743
Qin C, Moreno RG, Bowles C, et al., 2016, A semi-supervised large margin algorithm for white matter hyperintensity segmentation, 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Publisher: Springer Verlag, Pages: 104-112
Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation.
Guerrero R, Ledig C, Schmidt-Richberg A, et al., 2016, Group-constrained manifold learning: Application to AD risk assessment, PATTERN RECOGNITION, Vol: 63, Pages: 570-582, ISSN: 0031-3203
Kanavati F, Tong T, Misawa K, et al., 2016, Supervoxel Classification Forests for Estimating Pairwise Image Correspondences, Pattern Recognition, Vol: 63, Pages: 561-569, ISSN: 0031-3203
This article presents a general method for estimating pairwise image correspondences,which is a fundamental problem in image analysis. The method consistsof over-segmenting a pair of images into supervoxels. A forest classifier is thentrained on one of the images, the source, by using supervoxel indices as voxelwiseclass labels. Applying the forest on the other image, the target, yields asupervoxel labelling, which is then regularised using majority voting within theboundaries of the target’s supervoxels. This yields semi-dense correspondencesin a fully automatic, unsupervised, efficient and robust manner. The advantageof our approach is that no prior information or manual annotations arerequired, making it suitable as a general initialisation component for variousmedical imaging tasks that require coarse correspondences, such as atlas/patchbasedsegmentation, registration, and atlas construction. We demonstrate theeffectiveness of our approach in two different applications: a) initialisation oflongitudinal registration on spine CT data of 96 patients, and b) atlas-basedimage segmentation using 150 abdominal CT images. Comparison to state-ofthe-artmethods demonstrate the potential of supervoxel classification forestsfor estimating image correspondences.
Oktay O, Bai W, Guerrero R, et al., 2016, Stratified decision forests for accurate anatomical landmark localization, IEEE Transactions on Medical Imaging, Vol: 36, Pages: 332-342, ISSN: 0278-0062
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.
Cajanus A, Hall A, Liu Y, et al., 2016, Analysis of frontotemporal lobar degeneration patients using automated magnetic resonance imaging analyzing tool: the MRI characteristics of C9ORF72 expansion carriers, 10th International Conference on Frontotemporal Dementias, Publisher: WILEY-BLACKWELL, Pages: 387-387, ISSN: 0022-3042
Cnossen MC, Polinder S, Lingsma HF, et al., 2016, Variation in structure and process of care in traumatic brain injury: Provider profiles of European Neurotrauma Centers participating in the CENTER-TBI study, PLoS ONE, Vol: 11
© 2016 Cnossen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Methods: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. Results: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. Conclusion: Even among high-volume, specialized neurotrauma centers there is substantial variat
Tong T, Caballero J, Bhatia K, et al., 2016, Dictionary learning for medical image denoising, reconstruction, and segmentation, Machine Learning and Medical Imaging, Pages: 153-181, ISBN: 9780128040768
© 2016 Elsevier Inc. All rights reserved. Modeling data as sparse linear combinations of basis elements from a learnt dictionary has been widely used in signal processing and machine learning. The learnt dictionary, which is well adapted to specific data, has proven to be very effective in image restoration and classification tasks. In this chapter, we will review the most popular dictionary learning techniques such as K-SVD and online dictionary learning. We will also demonstrate how these techniques can be applied to medical imaging applications including image denoising, reconstruction, super-resolution and segmentation.
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