701 results found
Maas AIR, Menon DK, Steyerberg EW, et al., 2015, Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): a prospective longitudinal observational study., Neurosurgery, Vol: 76, Pages: 67-80
BACKGROUND: Current classification of traumatic brain injury (TBI) is suboptimal, and management is based on weak evidence, with little attempt to personalize treatment. A need exists for new precision medicine and stratified management approaches that incorporate emerging technologies. OBJECTIVE: To improve characterization and classification of TBI and to identify best clinical care, using comparative effectiveness research approaches. METHODS: This multicenter, longitudinal, prospective, observational study in 22 countries across Europe and Israel will collect detailed data from 5400 consenting patients, presenting within 24 hours of injury, with a clinical diagnosis of TBI and an indication for computed tomography. Broader registry-level data collection in approximately 20,000 patients will assess generalizability. Cross sectional comprehensive outcome assessments, including quality of life and neuropsychological testing, will be performed at 6 months. Longitudinal assessments will continue up to 24 months post TBI in patient subsets. Advanced neuroimaging and genomic and biomarker data will be used to improve characterization, and analyses will include neuroinformatics approaches to address variations in process and clinical care. Results will be integrated with living systematic reviews in a process of knowledge transfer. The study initiation was from October to December 2014, and the recruitment period was for 18 to 24 months. EXPECTED OUTCOMES: Collaborative European NeuroTrauma Effectiveness Research in TBI should provide novel multidimensional approaches to TBI characterization and classification, evidence to support treatment recommendations, and benchmarks for quality of care. Data and sample repositories will ensure opportunities for legacy research. DISCUSSION: Comparative effectiveness research provides an alternative to reductionistic clinical trials in restricted patient populations by exploiting differences in biology, care, and outcome to support
Baumgartner CF, Gomez A, Koch LM, et al., 2015, Self-Aligning Manifolds for Matching Disparate Medical Image Datasets., Inf Process Med Imaging, Vol: 24, Pages: 363-374, ISSN: 1011-2499
Manifold alignment can be used to reduce the dimensionality of multiple medical image datasets into a single globally consistent low-dimensional space. This may be desirable in a wide variety of problems, from fusion of different imaging modalities for Alzheimer's disease classification to 4DMR reconstruction from 2D MR slices. Unfortunately, most existing manifold alignment techniques require either a set of prior correspondences or comparability between the datasets in high-dimensional space, which is often not possible. We propose a novel technique for the 'self-alignment' of manifolds (SAM) from multiple dissimilar imaging datasets without prior correspondences or inter-dataset image comparisons. We quantitatively evaluate the method on 4DMR reconstruction from realistic, synthetic sagittal 2D MR slices from 6 volunteers and real data from 4 volunteers. Additionally, we demonstrate the technique for the compounding of two free breathing 3D ultrasound views from one volunteer. The proposed method performs significantly better for 4DMR reconstruction than state-of-the-art image-based techniques.
Parisot S, Arslan S, Passerat-Palmbach J, et al., 2015, Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex., Inf Process Med Imaging, Vol: 24, Pages: 600-612, ISSN: 1011-2499
The analysis of the connectome of the human brain provides key insight into the brain's organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori. In this paper, we propose a groupwise parcellation method of the cortex based on diffusion MR images (dMRI). We borrow ideas from the area of cosegmentation in computer vision and directly estimate a consistent parcellation across different subjects and scales through a spectral clustering approach. The parcellation is driven by the tractography connectivity profiles, and information between subjects and across scales. Promising qualitative and quantitative results on a sizeable data-set demonstrate the strong potential of the method.
Schmidt-Richberg A, Guerrero R, Ledig C, et al., 2015, Multi-stage Biomarker Models for Progression Estimation in Alzheimer's Disease., Pages: 387-398, ISSN: 1011-2499
The estimation of disease progression in Alzheimer's disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification.
Koch LM, Rajchl M, Tong T, et al., 2015, Multi-atlas Segmentation as a Graph Labelling Problem: Application to Partially Annotated Atlas Data., Pages: 221-232, ISSN: 1011-2499
Manually annotating images for multi-atlas segmentation is an expensive and often limiting factor in reliable automated segmentation of large databases. Segmentation methods requiring only a proportion of each atlas image to be labelled could potentially reduce the workload on expert raters tasked with labelling images. However, exploiting such a database of partially labelled atlases is not possible with state-of-the-art multi-atlas segmentation methods. In this paper we revisit the problem of multi-atlas segmentation and formulate its solution in terms of graph-labelling. Our graphical approach uses a Markov Random Field (MRF) formulation of the problem and constructs a graph connecting atlases and the target image. This provides a unifying framework for label propagation. More importantly, the proposed method can be used for segmentation using only partially labelled atlases. We furthermore provide an extension to an existing continuous MRF optimisation method to solve the proposed problem formulation. We show that the proposed method, applied to hippocampal segmentation of 202 subjects from the ADNI database, remains robust and accurate even when the proportion of manually labelled slices in the atlases is reduced to 20%.
Arslan S, Parisot S, Rueckert D, 2015, Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI., Inf Process Med Imaging, Vol: 24, Pages: 85-97, ISSN: 1011-2499
Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.
Keraudren K, Kuklisova-Murgasova M, Kyriakopoulou V, et al., 2014, Automated fetal brain segmentation from 2D MRI slices for motion correction, Neuroimage, Vol: 101, Pages: 633-643, ISSN: 1095-9572
Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39 weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.
Epton S, Bentley P, Ganesalingam J, et al., 2014, CTBRAIN MACHINE LEARNING PREDICTS STROKE THROMBOLYSIS RESULT, Meeting of the Associatiion-of-British-Neurologists, Publisher: BMJ PUBLISHING GROUP, ISSN: 0022-3050
Baumgartner CF, Kolbitsch C, Balfour DR, et al., 2014, High-resolution dynamic MR imaging of the thorax for respiratory motion correction of PET using groupwise manifold alignment, MEDICAL IMAGE ANALYSIS, Vol: 18, Pages: 939-952, ISSN: 1361-8415
Makropoulos A, Gousias IS, Ledig C, et al., 2014, Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 33, Pages: 1818-1831, ISSN: 0278-0062
Sohal M, Duckett SG, Zhuang X, et al., 2014, A prospective evaluation of cardiovascular magnetic resonance measures of dyssynchrony in the prediction of response to cardiac resynchronization therapy, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 16, ISSN: 1097-6647
Wolz R, Schwarz AJ, Yu P, et al., 2014, Robustness of automated hippocampal volumetry across magnetic resonance field strengths and repeat images, ALZHEIMERS & DEMENTIA, Vol: 10, Pages: 430-438, ISSN: 1552-5260
Guerrero R, Wolz R, Rao AW, et al., 2014, Manifold population modeling as a neuro-imaging biomarker: Application to ADNI and ADNI-GO, NEUROIMAGE, Vol: 94, Pages: 275-286, ISSN: 1053-8119
Tong T, Wolz R, Ga Q, et al., 2014, Multiple instance learning for classification of dementia in brain MRI, MEDICAL IMAGE ANALYSIS, Vol: 18, Pages: 808-818, ISSN: 1361-8415
Boardman JP, Walley A, Ball G, et al., 2014, Common Genetic Variants and Risk of Brain Injury After Preterm Birth, PEDIATRICS, Vol: 133, Pages: E1655-E1663, ISSN: 0031-4005
Tenovuo O, Menon D, van Gils M, et al., 2014, Improving the individual diagnostics of TBI-The international TBIcare project
Wright R, Kyriakopoulou V, Ledig C, et al., 2014, Automatic quantification of normal cortical folding patterns from fetal brain MRI, NEUROIMAGE, Vol: 91, Pages: 21-32, ISSN: 1053-8119
Kainz B, Voglreiter P, Sereinigg M, et al., 2014, High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study, 11th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 81-84
Data below 1 mm voxel size is getting more and more common in the clinical practice but it is still hard to obtain a consistent collection of such datasets for medical image processing research. With this paper we provide a large collection of Contrast Enhanced (CE) Computed Tomography (CT) data from porcine animal experiments and describe their acquisition procedure and peculiarities. We have acquired three CE-CT phases at the highest available scanner resolution of 57 porcine livers during induced respiratory arrest. These phases capture contrast enhanced hepatic arteries, portal venous veins and hepatic veins. Therefore, we provide scan data that allows for a highly accurate reconstruction of hepatic vessel trees. Several datasets have been acquired during Radio-Frequency Ablation (RFA) experiments. Hence, many datasets show also artificially induced hepatic lesions, which can be used for the evaluation of structure detection methods.
Kainz B, Keraudren K, Kyriakopoulou V, et al., 2014, Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors, 11th International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1230-1233
Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90.
karim R, 2014, A Method to Standardize Quantification of Left Atrial Scar From Delayed-Enhancement MR Images, IEEE Journal of Translational Engineering in Health and Medicine, ISSN: 2168-2372
Zang KY, Kedgley AE, Donoghue CD, et al., 2014, MORPHOLOGICAL STUDY OF LATERAL MENISCUS USING STATISTICAL SHAPE MODELLING: A STUDY USING DATA FROM THE OSTEOARTHRITIS INITIATIVE, World Congress of the Osteoarthritis-Research-Society-International (OARSI)
Caballero J, Price AN, Rueckert D, et al., 2014, Dictionary learning and time sparsity for dynamic MR data reconstruction, IEEE Transactions on Medical Imaging, Vol: 33, Pages: 979-994, ISSN: 1558-254X
Newcombe V, Ledig C, Abate G, et al., 2014, DYNAMIC EVOLUTION OF ATROPHY AFTER MODERATE TO SEVERE TRAUMATIC BRAIN INJURY, 11th Symposium of the International-Neurotrauma-Society
de Marvao A, Dawes TJW, Shi W, et al., 2014, Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power, Journal of Cardiovascular Magnetic Resonance, Vol: 16, ISSN: 1532-429X
Background: Cardiac phenotypes, such as left ventricular (LV) mass, demonstrate high heritability although mostgenes associated with these complex traits remain unidentified. Genome-wide association studies (GWAS) haverelied on conventional 2D cardiovascular magnetic resonance (CMR) as the gold-standard for phenotyping.However this technique is insensitive to the regional variations in wall thickness which are often associated with leftventricular hypertrophy and require large cohorts to reach significance. Here we test whether automated cardiacphenotyping using high spatial resolution CMR atlases can achieve improved precision for mapping wall thicknessin healthy populations and whether smaller sample sizes are required compared to conventional methods.Methods: LV short-axis cine images were acquired in 138 healthy volunteers using standard 2D imaging and 3Dhigh spatial resolution CMR. A multi-atlas technique was used to segment and co-register each image. Theagreement between methods for end-diastolic volume and mass was made using Bland-Altman analysis in 20subjects. The 3D and 2D segmentations of the LV were compared to manual labeling by the proportion ofconcordant voxels (Dice coefficient) and the distances separating corresponding points. Parametric andnonparametric data were analysed with paired t-tests and Wilcoxon signed-rank test respectively. Voxelwise powercalculations used the interstudy variances of wall thickness.Results: The 3D volumetric measurements showed no bias compared to 2D imaging. The segmented 3D imageswere more accurate than 2D images for defining the epicardium (Dice: 0.95 vs 0.93, P < 0.001; mean error 1.3 mmvs 2.2 mm, P < 0.001) and endocardium (Dice 0.95 vs 0.93, P < 0.001; mean error 1.1 mm vs 2.0 mm, P < 0.001). The3D technique resulted in significant differences in wall thickness assessment at the base, septum and apex of theLV compared to 2D (P < 0.001). Fewer subjects were required for 3D imaging to detect a 1 mm d
Bhatia KK, Rao A, Price AN, et al., 2014, Hierarchical manifold learning for regional image analysis., IEEE Trans Med Imaging, Vol: 33, Pages: 444-461
We present a novel method of hierarchical manifold learning which aims to automatically discover regional properties of image datasets. While traditional manifold learning methods have become widely used for dimensionality reduction in medical imaging, they suffer from only being able to consider whole images as single data points. We extend conventional techniques by additionally examining local variations, in order to produce spatially-varying manifold embeddings that characterize a given dataset. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate the utility of our method in two very different settings: 1) to learn the regional correlations in motion within a sequence of time-resolved MR images of the thoracic cavity; 2) to find discriminative regions of 3-D brain MR images associated with neurodegenerative disease.
Rueckert D, Schnabel JA, 2014, Registration and segmentation in medical imaging, Pages: 137-156, ISBN: 9783642449062
The analysis of medical images plays an increasingly important role in many clinical applications. Different imaging modalities often provide complementary anatomical information about the underlying tissues such as the X-ray attenuation coefficients from X-ray computed tomography (CT), and proton density or proton relaxation times from magnetic resonance (MR) imaging. The images allow clinicians to gather information about the size, shape and spatial relationship between anatomical structures and any pathology, if present. Other imaging modalities provide functional information such as the blood flow or glucose metabolism from positron emission tomography (PET) or single-photon emission tomography (SPECT), and permit clinicians to study the relationship between anatomy and physiology. Finally, histological images provide another important source of information which depicts structures at a microscopic level of resolution. © 2014 Springer-Verlag Berlin Heidelberg.
Rueckert D, Wolz R, Aljabar P, 2014, Machine learning meets medical imaging: Learning and discovery of clinically useful information from images, 4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE)
Gao Q, Asthana A, Tong T, et al., 2014, Multi-scale Feature Learning on Pixels and Super-pixels for Seminal Vesicles MRI Segmentation, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Gao Q, Tong T, Rueckert D, et al., 2014, Multi-Atlas Propagation via a Manifold Graph on a Database of Both Labeled and Unlabeled Images, Conference on Medical Imaging - Computer-Aided Diagnosis, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Bentley P, Ganesalingam J, Jones ALC, et al., 2014, Prediction of stroke thrombolysis outcome using CT brain machine learning, NEUROIMAGE-CLINICAL, Vol: 4, Pages: 635-640, ISSN: 2213-1582
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