700 results found
Tong T, Wolz R, Gao Q, et al., 2013, Multiple Instance Learning for Classification of Dementia in Brain MRI, 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 599-606, ISSN: 0302-9743
Chu C, Oda M, Kitasaka T, et al., 2013, Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images, 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 165-172, ISSN: 0302-9743
Chabiniok R, Wong J, Giese D, et al., 2013, Flow Analysis in Cardiac Chambers Combining Phase Contrast, 3D Tagged and Cine MRI, 7th International Conference on Functional Imaging and Modeling of the Heart (FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 360-369, ISSN: 0302-9743
Zhuang X, Shi W, Wang H, et al., 2013, Computation on shape manifold for atlas generation: application to whole heart segmentation of cardiac MRI, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Baumgartner CF, Kolbitsch C, McClelland JR, et al., 2013, Groupwise simultaneous manifold alignment for high-resolution dynamic MR imaging of respiratory motion., Pages: 232-243, ISSN: 1011-2499
Respiratory motion is a complicating factor for many applications in medical imaging and there is significant interest in dynamic imaging that can be used to estimate such motion. Magnetic resonance imaging (MRI) is an attractive modality for motion estimation but current techniques cannot achieve good image contrast inside the lungs. Manifold learning is a powerful tool to discover the underlying structure of high-dimensional data. Aligning the manifolds of multiple datasets can be useful to establish relationships between different types of data. However, the current state-of-the-art in manifold alignment is not robust to the wide variations in manifold structure that may occur in clinical datasets. In this work we propose a novel, fully automatic technique for the simultaneous alignment of large numbers of manifolds with varying manifold structure. We apply the technique to reconstruct high-resolution and high-contrast dynamic 3D MRI images from multiple 2D datasets for the purpose of respiratory motion estimation. The proposed method is validated on synthetic data with known ground truth and real data. We demonstrate that our approach can be applied to reconstruct significantly more accurate and consistent dynamic images of the lungs compared to the current state-of-the-art in manifold alignment.
Gao Q, Chang PL, Rueckert D, et al., 2013, Modeling of the bony pelvis from MRI using a multi-atlas AE-SDM for registrationand tracking in image-guided robotic prostatectomy, Computerized Medical Imaging and Graphics
Munoz-Ruiz MA, Hartikainen P, Koikkalainen J, et al., 2012, Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal Volumetry, Tensor-Based Morphometry and Voxel-Based Morphometry, PLOS ONE, Vol: 7, ISSN: 1932-6203
Gousias IS, Hammers A, Counsell SJ, et al., 2012, Automatic segmentation of pediatric brain MRIs using a maximum probability pediatric atlas, Pages: 95-100
Automatic anatomical segmentation of pediatric brain MR data sets can be pursued with the use of registration algorithms when segmentation priors (atlases) are in hand. We investigated the performance of a maximum probability pediatric atlas (MPPA), template based registration and label propagation. The MPPA was created from the 33 pediatric data sets, available through www.brain-development.org. We evaluated the performance of the MPPA comparing with manual segmentations by means of the Dice overlap coefficient. Dice values, averaged across representative regions, were 0.90 ± 0.03 for the hippocampus, 0.92 ± 0.01 for the caudate nucleus and 0.92 ± 0.02 for the pre-central gyrus. Segmentations of 36 further unsegmented target 3T images (1-year-olds and 2-year-olds) yielded visibly high-quality results. This registration approach allows the rapid construction of automatically labeled pediatric brain atlases in a single registration step. © 2012 IEEE.
Deligianni F, Varoquaux G, Thirion B, et al., 2012, Relating brain functional connectivity to anatomical connections: Model selection, Pages: 178-185, ISSN: 0302-9743
We aim to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity. Following , we formulate this problem as estimating a multivariate autoregressive (MAR) model with sparse linear regression. We introduce a model selection framework based on cross-validation. We select the appropriate sparsity of the connectivity matrices and demonstrate that choosing an ordering for the MAR that lends to sparser models is more appropriate than a random. Finally, we suggest randomized Least Absolute Shrinkage and Selective Operator (LASSO) in order to identify relevant anatomo-functional links with better recovery of ground truth. © 2012 Springer-Verlag.
Pszczolkowski S, Pizarro L, O'Regan DP, et al., 2012, Gradient projection learning for parametric nonrigid registration, Pages: 226-233, ISSN: 0302-9743
A potentially large anatomical variability among subjects in a population makes nonrigid image registration techniques prone to inaccuracies and to high computational costs in their optimisation. In this paper, we propose a new learning-based approach to accelerate the convergence rate of any chosen parametric energy-based image registration method. From a set of training images and their corresponding deformations, our method learns offline a projection from the gradient space of the energy functional to the parameter space of the chosen registration method using partial least squares. Combined with a regularisation term, the learnt projection is subsequently used online to approximate the optimisation of the energy functional for unseen images. We employ the B-spline approach as underlying registration method, but other parametric methods can be used as well. We perform experiments on synthetic image data and MR cardiac sequences to show that our approach significantly accelerates the convergence -in number of iterations and total computational cost- of the chosen registration method, while achieving similar results in terms of accuracy. © 2012 Springer-Verlag.
Guerrero R, Donoghue CR, Pizarro L, et al., 2012, Learning correspondences in knee MR images from the osteoarthritis initiative, Pages: 218-225, ISSN: 0302-9743
Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration of images of the knee is challenging to achieve using intensity based registration algorithms. Problems arise due to large anatomical inter-subject differences which causes registrations to fail to converge to an accurate solution. In this work we propose learning correspondences in pairs of images to match self-similarity features, that describe images in terms of their local structure rather than their intensity. We use RANSAC as a robust model estimator. We show a substantial improvement in terms of mean error and standard deviation of 2.13mm and 2.47mm over intensity based registration methods, when comparing landmark alignment error. © 2012 Springer-Verlag.
Serag A, Gousias IS, Makropoulos A, et al., 2012, Unsupervised learning of shape complexity: Application to brain development, Pages: 88-99, ISSN: 0302-9743
This paper presents a framework for unsupervised learning of shape complexity in the developing brain. It learns the complexity in different brain structures by applying several shape complexity measures to each individual structure, and then using feature selection to select the measures that best describe the changes in complexity of each structure. Then, feature selection is applied again to assign a score to each structure, in order to find which structure can be a good biomarker of brain development. This study was carried out using T2-weighted MR images from 224 premature neonates (the age range at the time of scan was 26.7 to 44.86 weeks post-menstrual age). The advantage of the proposed framework is that one can extract as many ROIs as desired, and the framework automatically finds the ones which can be used as good biomarkers. However, the example application focuses on neonatal brain image data, the proposed framework for combining information from multiple measures may be applied more generally to other populations and other forms of imaging data. © 2012 Springer-Verlag.
van Rossum IA, Vos SJB, Burns L, et al., 2012, Injury markers predict time to dementia in subjects with MCI and amyloid pathology, NEUROLOGY, Vol: 79, Pages: 1809-1816, ISSN: 0028-3878
Vos S, van Rossum I, Burns L, et al., 2012, Test sequence of CSF and MRI biomarkers for prediction of AD in subjects with MCI, NEUROBIOLOGY OF AGING, Vol: 33, Pages: 2272-2281, ISSN: 0197-4580
Gousias IS, Edwards AD, Rutherford MA, et al., 2012, Magnetic resonance imaging of the newborn brain: Manual segmentation of labelled atlases in term-born and preterm infants, NEUROIMAGE, Vol: 62, Pages: 1499-1509, ISSN: 1053-8119
Donoghue CR, Rao A, Pizarro L, et al., 2012, Fast and accurate global geodesic registrations using knee MRI from the Osteoarthritis Initiative, Pages: 50-57, ISSN: 2160-7508
Registration is important for many applications in medical image analysis. Affine registration of knee MR images can suffer failures due to large anatomical and articulated pose variations. This work is demonstrated using 2743 MR images from the Osteoarthritis Initiative (OAI) public access dataset. With such large datasets any manual interventions to aid registration success are not feasible and so full automation with high accuracy is of paramount importance. Additionally, computing exhaustive pairwise registrations across the OAI dataset is very computationally expensive. We present a sparse geodesic registration method that increases accuracy of pairwise registration and also enables fast online computation of registration. We then propose two novel methods to reduce registration error over the graph. Firstly we use all precomputed transformations to infer transformation errors for each edge, through assuming global registration cycle consistency across a sparse graph. In conjunction with this, we suggest fusing multiple successful registrations as a strategy to mitigate small errors in each transformation in the graph. It is shown that, in combination, these techniques achieve more accurate pairwise registration results than both geodesic registration and direct pairwise registration. This paper addresses accuracy of registrations, speed of online computation and is demonstrated on a large scale dataset. © 2012 IEEE.
Frangi A, Santos A, Ober R, et al., 2012, Welcome
Dawant BM, Christensen GE, Fitzpatrick JM, et al., 2012, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, ISBN: 9783642313394
Donoghue CR, Rao A, Bull AMJ, et al., 2012, Robust global registration through geodesic paths on an empirical manifold with knee MRI from the Osteoarthritis Initiative (OAI), Pages: 1-10, ISSN: 0302-9743
Accurate affine registrations are crucial for many applications in medical image analysis. Within the Osteoarthritis Initiative (OAI) dataset we have observed a failure rate of approximately 4% for direct affine registrations of knee MRI without manual initialisation. Despite this, the problem of robust affine registration has not received much attention in recent years. With the increase in large medical image datasets, manual intervention is not a suitable solution to achieve successful affine registrations. We introduce a framework to improve the robustness of affine registrations without prior manual initialisations. We use 10,307 MR images from the large dataset available from the OAI to model the low dimensional manifold of the population of unregistered knee MRIs as a sparse k-nearest-neighbour graph. Affine registrations are computed in advance for nearest neighbours only. When a pairwise image registration is required the shortest path across the graph is extracted to find a geodesic path on the empirical manifold. The precomputed affine transformations on this path are composed to find an estimated transformation. Finally a refinement step is used to further improve registration accuracy. Failure rates of geodesic affine registrations reduce to 0.86% with the registration framework proposed. © 2012 Springer-Verlag.
O'Regan DP, Shi W, Ariff B, et al., 2012, Remodeling after acute myocardial infarction: mapping ventricular dilatation using three dimensional CMR image registration, Journal of Cardiovascular Magnetic Resonance, Vol: 14, ISSN: 1532-429X
Background: Progressive heart failure due to remodeling is a major cause of morbidity and mortality followingmyocardial infarction. Conventional clinical imaging measures global volume changes, and currently there is nomeans of assessing regional myocardial dilatation in relation to ischemic burden. Here we use 3D co-registration ofCardiovascular Magnetic Resonance (CMR) images to assess the long-term effects of ischemia-reperfusion injury onleft ventricular structure after acute ST-elevation myocardial infarction (STEMI).Methods: Forty six patients (age range 33–77 years) underwent CMR imaging within 7 days following primarypercutaneous coronary intervention (PPCI) for acute STEMI with follow-up at one year. Functional cine imaging andLate Gadolinium Enhancement (LGE) were segmented and co-registered. Local left ventricular wall dilatation wasassessed by using intensity-based similarities to track the structural changes in the heart between baseline andfollow-up. Results are expressed as means, standard errors and 95% confidence interval (CI) of the difference.Results: Local left ventricular remodeling within infarcted myocardium was greater than in non-infarctedmyocardium (1.6% ± 1.0 vs 0.3% ± 0.9, 95% CI: -2.4% – -0.2%, P = 0.02). One-way ANOVA revealed that transmuralinfarct thickness had a significant effect on the degree of local remodeling at one year (P < 0.0001) with greatestwall dilatation observed when infarct transmurality exceeded 50%. Infarct remodeling was more severe whenmicrovascular obstruction (MVO) was present (3.8% ± 1.3 vs −1.6% ± 1.4, 95% CI: -9.1% – -1.5%, P = 0.007) and whenend-diastolic volume had increased by >20% (4.8% ± 1.4 vs −0.15% ± 1.2, 95% CI: -8.9% – -0.9%, P = 0.017).Conclusions: The severity of ischemic injury has a significant effect on local ventricular wall remodeling with onlymodest dilatation observed within non-ischemic myocardium. Limitatio
Shi W, Zhuang X, Wang H, et al., 2012, A Comprehensive Cardiac Motion Estimation Framework Using Both Untagged and 3-D Tagged MR Images Based on Nonrigid Registration, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 31, Pages: 1263-1275, ISSN: 0278-0062
Duckett SG, Shi W, Zhuang X, et al., 2012, CARDIAC MRI: UNDERSTANDING MYOCARDIAL MOTION TO PREDICT REMODELLING PRE CARDIAC RESYNCHRONISATION THERAPY, Annual Conference of the British-Cardiovascular-Society (BCS)
Malcolme-Lawes L, Juli C, Karim R, et al., 2012, AUTOMATED ANALYSIS OF ATRIAL DELAYED ENHANCEMENT CARDIAC MRI CORRELATES WITH VOLTAGE, AF RECURRENCE POST-ABLATION, AND HIGH STROKE RISK, Annual Conference of the British-Cardiovascular-Society (BCS)
Wolz R, Aljabar P, Hajnal JV, et al., 2012, Nonlinear dimensionality reduction combining MR imaging with non-imaging information, MEDICAL IMAGE ANALYSIS, Vol: 16, Pages: 819-830, ISSN: 1361-8415
Ball G, Boardman JP, Rueckert D, et al., 2012, The Effect of Preterm Birth on Thalamic and Cortical Development, CEREBRAL CORTEX, Vol: 22, Pages: 1016-1024, ISSN: 1047-3211
Keihaninejad S, Heckemann RA, Gousias IS, et al., 2012, Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation, PLOS ONE, Vol: 7, ISSN: 1932-6203
Hu M, Penney G, Figl M, et al., 2012, Reconstruction of a 3D surface from video that is robust to missing data and outliers: Application to minimally invasive surgery using stereo and mono endoscopes, MEDICAL IMAGE ANALYSIS, Vol: 16, Pages: 597-611, ISSN: 1361-8415
Dhutia NM, Cole GD, Willson K, et al., 2012, A new automated system to identify a consistent sampling position to make tissue Doppler and transmitral Doppler measurements of E, E ' and E/E ', INTERNATIONAL JOURNAL OF CARDIOLOGY, Vol: 155, Pages: 394-399, ISSN: 0167-5273
Wang H, Shi W, Zhuang X, et al., 2012, Automatic cardiac motion tracking using both untagged and 3D tagged MR images, Pages: 45-54, ISSN: 0302-9743
We present a fully automatic framework for cardiac motion tracking based on non-rigid image registration for the analysis of myocardial motion using both untagged and 3D tagged MR images. We detect and track anatomical landmarks in the heart and combine this with intensity-based motion tracking to allow accurately model cardiac motion while significantly reduce the computational complexity. A collaborative similarity measure simultaneously computed in three LA views is employed to register a sequence of images taken during the cardiac cycle to a reference image taken at end-diastole. We then integrate a valve plane tracker into the framework which uses short-axis and long-axis untagged MR images as well as 3D tagged images to estimate a fully four-dimensional motion field of the left ventricle. © 2012 Springer-Verlag.
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