Publications
1015 results found
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.
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.
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
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
- Author Web Link
- Cite
- Citations: 61
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
- Author Web Link
- Cite
- Citations: 40
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)
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)
- Author Web Link
- Cite
- Citations: 1
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
- Author Web Link
- Cite
- Citations: 216
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
- Author Web Link
- Cite
- Citations: 49
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
- Author Web Link
- Cite
- Citations: 30
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
- Author Web Link
- Cite
- Citations: 7
Karim R, Arujuna A, Brazier A, et al., 2012, Validation of a novel method for the automatic segmentation of left atrial scar from delayed-enhancement magnetic resonance, Pages: 254-262, ISSN: 0302-9743
Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre- and post- radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction, making them tedious and prone to high observer variability. In this paper, a novel automatic segmentation algorithm for segmenting LA scar was validated using digital phantoms and clinical data from 11 patients. The performance of the approach was compared to the two leading semi-automatic techniques and the ground truth of manual segmentations by 2 expert observers. The novel approach was shown to be accurate in terms of Dice coefficient, robust to typical image intensity variability, and much faster in terms of execution time. © 2012 Springer-Verlag.
Shi W, Zhuang X, Wolz R, et al., 2012, A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation, Pages: 178-187, ISSN: 0302-9743
Registration and segmentation uncertainty may be important information to convey to a user when automatic image analysis is performed. Uncertainty information may be used to provide additional diagnostic information to traditional analysis of cardiac function. In this paper, we develop a framework for the automatic segmentation of the cardiac anatomy from multiple MR images. We also define the registration and segmentation uncertainty and explore its use for diagnostic purposes. Our framework uses cardiac MR image sequences that are widely available in clinical practice. We improve the performance of the cardiac segmentation algorithms by combining information from multiple MR images using a graph-cut based segmentation. We evaluate this framework on images from 32 subjects: 13 patients with ischemic cardiomyopathy, 14 patients with dilated cardiomyopathy and 5 normal volunteers. Our results indicate that the proposed method is capable of producing segmentation results with very high robustness and high accuracy with minimal user interaction across all subject groups. We also show that registration and segmentation uncertainties are good indicators for segmentation failures as well as good predictors for the functional abnormality of the subject. © 2012 Springer-Verlag.
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.
Vounou M, Janousova E, Wolz R, et al., 2012, Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease, NEUROIMAGE, Vol: 60, Pages: 700-716, ISSN: 1053-8119
- Author Web Link
- Cite
- Citations: 106
Gray KR, Wolz R, Heckemann RA, et al., 2012, Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease, NEUROIMAGE, Vol: 60, Pages: 221-229, ISSN: 1053-8119
- Author Web Link
- Cite
- Citations: 118
van der Lijn F, Verhaaren BFJ, Ikram MA, et al., 2012, Automated measurement of local white matter lesion volume, NEUROIMAGE, Vol: 59, Pages: 3901-3908, ISSN: 1053-8119
- Author Web Link
- Cite
- Citations: 12
Serag A, Aljabar P, Ball G, et al., 2012, Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression, NEUROIMAGE, Vol: 59, Pages: 2255-2265, ISSN: 1053-8119
- Author Web Link
- Cite
- Citations: 209
Cardoso MJ, Wolz R, Modat M, et al., 2012, Geodesic information flows, Pages: 262-270, ISSN: 0302-9743
© Springer-Verlag Berlin Heidelberg 2012. Homogenising the availability of manually generated information in large databases has been a key challenge of medical imaging for many years. Due to the time consuming nature of manually segmenting, parcellating and localising landmarks in medical images, these sources of information tend to be scarce and limited to small, and sometimes morphologically similar, subsets of data. In this work we explore a new framework where these sources of information can be propagated to morphologically dissimilar images by diffusing and mapping the information through intermediate steps. The spatially variant data embedding uses the local morphology and intensity similarity between images to diffuse the information only between locally similar images. This framework can thus be used to propagate any information from any group of subject to every other subject in a database with great accuracy. Comparison to state-of-the-art propagation methods showed highly statistically significant (p < 10−4) improvements in accuracy when propagating both structural parcelations and brain segmentations geodesically.
Shi W, Zhuang X, Pizarro L, et al., 2012, Registration using sparse free-form deformations, Pages: 659-666, ISSN: 0302-9743
Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.
Bhatia KK, Price AN, Hajnal JV, et al., 2012, Localised manifold learning for cardiac image analysis, Conference on Medical Imaging - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Soininen H, Mattila J, Koikkalainen J, et al., 2012, Software Tool for Improved Prediction of Alzheimer's Disease, 10th International Conference on Alzheimers and Parkinsons Diseases (AD/PD), Publisher: KARGER, Pages: 149-152, ISSN: 1660-2854
- Author Web Link
- Cite
- Citations: 10
Vialard F-X, Rissier L, Cotter CJ, et al., 2012, Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation, International Journal of Computer Vision
In the context of large deformations by diffeomorphisms, we propose a new diffeomorphic registration algorithm for 3D images that performs the optimization directly on the set of geodesic flows. The key contribution of this work is to provide an accurate estimation of the so-called initial momentum, which is a scalar function encoding the optimal deformation between two images through the Hamiltonian equations of geodesics. Since the initial momentum has proven to be a key tool for statistics on shape spaces, our algorithm enables more reliable statistical comparisons for 3D images.Our proposed algorithm is a gradient descent on the initial momentum, where the gradient is calculated using standard methods from optimal control theory. To improve the numerical efficiency of the gradient computation, we have developed an integral formulation of the adjoint equations associated with the geodesic equations.We then apply it successfully to the registration of 2D phantom images and 3D cerebral images. By comparing our algorithm to the standard approach of Beg et al. (Int. J. Comput. Vis. 61:139–157, 2005), we show that it provides a more reliable estimation of the initial momentum for the optimal path. In addition to promising statistical applications, we finally discuss different perspectives opened by this work, in particular in the new field of longitudinal analysis of biomedical images.
Lotjonen J, Wolz R, Koikkalainen J, et al., 2012, HIPPOCAMPAL ATROPHY RATE USING AN EXPECTATION MAXIMIZATION CLASSIFIER WITH A DISEASE-SPECIFIC PRIOR, 9th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1164-1167
Luong DVN, Parpas P, Rueckert D, et al., 2012, Solving MRF Minimization by Mirror Descent, 8th International Symposium on Visual Computing (ISVC), Publisher: SPRINGER-VERLAG BERLIN, Pages: 587-598, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 7
Tung KP, Shi WZ, Pizarro L, et al., 2012, Automatic Detection of Coronary Stent Struts in Intravascular OCT imaging, Conference on Medical Imaging - Computer-Aided Diagnosis, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
- Author Web Link
- Cite
- Citations: 5
Wolz R, Chu C, Misawa K, et al., 2012, Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 10-17, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 68
Bhatia KK, Rao A, Price AN, et al., 2012, Hierarchical Manifold Learning, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 512-519, ISSN: 0302-9743
- Author Web Link
- Cite
- Citations: 9
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.