Publications 2014


Journal publications


  • K. Bhatia, A. Rao, A. Price, R. Wolz, J. Hajnal, and D. Rueckert. Hierarchical manifold learning for regional image analysis. IEEE Transactions on Medical Imaging, 33(2): 444-461, 2014.
  • J. Caballero, A. Price, D. Rueckert and J. V. Hajnal. Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Transactions on Medical Imaging, 33(4): 979-994, 2014.
  • R. Wright, V. Kyriakopoulou, C. Ledig, M.A. Rutherford, J.V. Hajnal, D. Rueckert and P. Aljabar. Automatic Quantification of Normal Cortical Folding Patterns from Fetal Brain. NeuroImage, 91:21-32, 2014.
  • K. Y. Zhang, A. E. Kedgley, C. R. Donoghue, D. Rueckert and A. M. J. Bull. The relationship between lateral meniscus shape and joint contact parameters in the knee: A study using data from the Osteoarthritis Initiative. Arthritis Research & Therapy, 16:R27:1-9, 2014.
  • A. S. Pandit, E. Robinson, P. Aljabar, G. Ball, I. S. Gousias, Z. Wang, J. V. Hajnal, D. Rueckert, S. J. Counsell, G. Montana and A. D. Edwards. Whole-Brain Mapping of Structural Connectivity in Infants Reveals Altered Connection Strength Associated with Growth and Preterm Birth. Cerebral Cortex, 24(9):2324-33., 2014.
  • A. de Marvao, T. J. W. Dawes, W. Shi, C. Minas, N. G. Keenan, T. Diamond, G. Durighel, G. Montana, D. Rueckert, S. A. Cook and D. P. O’Regan. Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power. Journal of Cardiovascular Magnetic Resonance 2014, 16:16
  • P. Bentley, J. Ganesalingam, A. Dias, K. Mahady, S. Epton, P. Rinne, P. Sharma, O. Halse, A. Mehta and D. Rueckert. Prediction of stroke thrombolysis outcome using CT brain machine learning. NeuroImage Clinical, 4:635-640, 2014.
  • R. Karim, A. Arujuna, R. J. Housden, J. Gill, K. Matharu, J. Gill, C. A. Rinaldi, M. O’Neill, D. Rueckert, R. Razavi, T. Schaeffter and K. Rhode. A Method to Standardize Quantification of Left Atrial Scar from Delayed-Enhancement MR Images. IEEE Translational Engineering in Health and Medicine, 2, 2014.
  • R. Guerrero, R. Wolz, A.W. Rao and D. Rueckert. Manifold population modeling as a neuro-imaging biomarker: Application to ADNI and ADNI-GO. NeuroImage, 94:275–286, 2014.
  • T. Tong, R. Wolz, Q. Gao, R. Guerrero, J. V. Hajnal and D. Rueckert. Multiple Instance Learning for Classification of Dementia in Brain MRI. Medical Image Analysis, 18(5):808-818, 2014.
  • A. Makropoulos, I. S. Gousias, C. Ledig, P. Aljabar, A. Serag, J. V. Hajnal, A. D. Edwards, S. J. Counsell and D. Rueckert. Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain. IEEE Transactions on Medical Imaging, 33(9):1818-31, 2014.
  • G. Ball, P. Aljabar, S. Zebari, N. Tusor, T. Arichi, N. Merchant, E. C. Robinson, E. Ogundipe, D. Rueckert, A. D. Edwards and S. J. Counsell. Rich-club organization of the newborn human brain. Proceedings of the National Academy of Sciences (PNAS), 111(20):7456–7461, 2014.
  • J. P. Boardman, A. Walley, G. Ball, P. Takousis, M. L. Krishnan, L. Hughes-Carre, P. Aljabar, A. Serag, C. King, N. Merchant, L. Srinivasan, P. Froguel, J. Hajnal, D. Rueckert, S. Counsell and A. D. Edwards. Common genetic variants and risk of brain injury after preterm birth. Pediatrics, 133(6): e1655-e1663, 2014.
  • C. F. Baumgartner, C. Kolbitsch, D. R. Balfour, P. K. Marsden, J. R. McClelland, D. Rueckert and A. P. King. High-Resolution Dynamic MR Imaging of the Thorax for Respiratory Motion Correction of PET Using Groupwise Manifold Alignment. Medical Image Analysis,18(7):939–952, 2014.
  • M. Sohal, S. G. Duckett, X. Zhuang, W. Shi, M. Ginks, A. Shetty, E. Sammut, S. Kozerke, S. Niederer, N. Smith, S. Ourselin, C. A. Rinaldi, D. Rueckert, G. Carr-White and R. Razavi. A Prospective Evaluation of Cardiac Magnetic Resonance Measures of Dyssynchrony in the Prediction of Response to Cardiac Resynchronization Therapy. Journal of Cardiovascular Magnetic Resonance, 16:5, 2014.
  • R. Wolz, A. J. Schwarz, P. Yu, P. E. Cole, D. Rueckert, C. R. Jack, D. Raunig, D. Hill. Robustness of automated hippocampal volumetry across magnetic resonance field strengths and repeat images. Alzheimer’s & Dementia 10(4):430-438, 2014.
  • K. Keraudren, B. Kainz, D. Rueckert, M. Kuklisova-Murgasova, V. Kyriakopoulou, C. Malamateniou, M. A. Rutherford, J. V. Hajnal. Automated fetal brain segmentation from 2D MRI slices for motion correction. NeuroImage, 101:633-43, 2014.
  • S. Vasylechko, C. Malamateniou, R. Nunes, M. Fox, J. Allsop, M. Rutherford, D. Rueckert and J. Hajnal. T2* relaxometry of fetal brain at 1.5 Tesla using a motion tolerant method. Magnetic Resonance in Medicine, doi:10.1002/mrm.25299, 2014.


Conference publications


  • Q. Gao, A. Asthana, T. Tong, D. Rueckert, E. Edwards. Multi-scale feature learning on pixels and super-pixels for seminal vesicles MRI segmentation. SPIE Medical Imaging, 903407, 2014.
  • B. Kainz, P. Voglreiter, M. Sereinigg, I. Wiederstein-Grasser, U. Mayrhauser, S. Kostenbauer, M. Pollari, R. Khlebnikov, M. Seise, T. Alhonnoro, Y. Häme, D. Seider, R. Flanagan, C. Bost, J. Muehl, D. O'Neill, T. Peng, S. J. Payne, D. Rueckert, D. Schmalstieg, M. Moche, M. Kolesnik, P. Stiegler and R. H. Portugaller: High-resolution contrast enhanced multi-phase hepatic Computed Tomography data fromaporcine Radio-Frequency Ablation study. IEEE International Symposium on Biomedical Imaging (ISBI): 81-84, 2014.
  • J. Koikkalainen, J. Lötjönen, C. Ledig, D. Rueckert, O. Tenovuo and D. Menon: Automatic quantification of CT images for traumatic brain injury. IEEE International Symposium on Biomedical Imaging (ISBI): 125-128, 2014.
  • L. Liu, W. Shi, D. Rueckert, M. Hu, S. Ourselin and X. Zhuang: Coronary centerline extraction based on ostium detection and model-guided directional minimal path. IEEE International Symposium on Biomedical Imaging (ISBI): 133-136, 2014.
  • A. Rao, C. Ledig, V. Newcombe, D. Menon and D. Rueckert: Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework. IEEE International Symposium on Biomedical Imaging (ISBI): 333-336, 2014.
  • C. F. Baumgartner, C. Kolbitsch, J. R. McClelland, D. Rueckert and A. P. King: Autoadaptive motion modelling. IEEE International Symposium on Biomedical Imaging (ISBI): 457-460, 2014.
  • S. Pszczólkowski, S. Zafeiriou, C. Ledig and D. Rueckert: A robust similarity measure for nonrigid image registration with outliers. IEEE International Symposium on Biomedical Imaging (ISBI): 568-571, 2014.
  • C. Ledig, W. Shi, A. Makropoulos, J. Koikkalainen, R. A. Heckemann, A. Hammers, J. Lötjönen, O. Tenovuo and D. Rueckert: Consistent and robust 4D whole-brain segmentation: Application to traumatic brain injury. IEEE International Symposium on Biomedical Imaging (ISBI): 673-676, 2014.
  • J. Lötjönen, C. Ledig, J. Koikkalainen, R. Wolz, L. Thurfjell, H. Soininen, S. Ourselin and D. Rueckert: Extended boundary shift integral. IEEE International Symposium on Biomedical Imaging (ISBI): 854-857, 2014.
  • K. K. Bhatia, A. N. Price, W. Shi, J. V. Hajnal and D. Rueckert: Super-resolution reconstruction of cardiac MRI using coupled dictionary learning. IEEE International Symposium on Biomedical Imaging (ISBI): 947-950, 2014.
  • X. Wu, R. J. Housden, Y. Ma, K. S. Rhode and D. Rueckert: A fast catheter segmentation and tracking from echocardiographic sequences based on corresponding X-ray fluoroscopic image segmentation and hierarchical graph modelling. IEEE International Symposium on Biomedical Imaging (ISBI): 951-954, 2014.
  • C. R. Donoghue, A. Rao, A. M. J. Bull and D. Rueckert: Learning osteoarthritis imaging biomarkers using Laplacian eigenmap embeddings with data from the OAI. IEEE International Symposium on Biomedical Imaging (ISBI): 1011-1014, 2014.
  • B. Kainz, K. Keraudren, V. Kyriakopoulou, M. A. Rutherford, J. V. Hajnal and D. Rueckert: Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors. IEEE International Symposium on Biomedical Imaging (ISBI): 1230-1233, 2014.
  • C. Ledig, W. Shi, W. Bai and Daniel Rueckert: Patch-Based Evaluation of Image Segmentation. IEEE Computer Vision and Pattern Recognition (CVPR): 3065-3072, 2014.
  • L. M. Koch, R. Wright, D. Vatansever, V. Kyriakopoulou, C. Malamateniou, P. A. Patkee, M. A. Rutherford, J. V. Hajnal, P. Aljabar and D. Rueckert: Graph-Based Label Propagation in Fetal Brain MR Images. Workshop on Machine Learning in Medical Imaging (MLMI): 9-16, 2014.
  • R. Guerrero, C. Ledig and D. Rueckert: Manifold Alignment and Transfer Learning for Classification of Alzheimer's Disease. Workshop on Machine Learning in Medical Imaging (MLMI): 77-84, 2014.
  • A. Schuh, M. Murgasova, A. Makropoulos, C. Ledig, S. J. Counsell, J. V. Hajnal, P. Aljabar and D. Rueckert. Construction of a 4D Brain Atlas and Growth Model using Diffeomorphic Registration. Workshop on Spatiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA), 2014.
  • J. Caballero, W. Bai, A. N. Price, D. Rueckert and J. V. Hajnal: Application-Driven MRI: Joint Reconstruction and Segmentation from Undersampled MRI Data. Medical Image Computing and Computer Assisted Interventions (MICCAI): 106-113, 2014.
  • B. Kainz, C. Malamateniou, M. Murgasova, K. Keraudren, M. A. Rutherford, J. V. Hajnal and D. Rueckert: Motion Corrected 3D Reconstruction of the Fetal Thorax from Prenatal MRI. Medical Image Computing and Computer Assisted Interventions (MICCAI): 284-291, 2014.
  • W. Shi, H. Lombaert, W. Bai, C. Ledig, X. Zhuang, A. M. Simoes Monteiro de Marvao, T. Dawes, D. P. O'Regan and D. Rueckert: Multi-atlas Spectral PatchMatch: Application to Cardiac Image Segmentation. Medical Image Computing and Computer Assisted Interventions (MICCAI): 348-355, 2014.
  • Z. Wang, K. K. Bhatia, B. Glocker, A. M. Simoes Monteiro de Marvao, T. Dawes, K. Misawa, K. Mori and D. Rueckert: Geodesic Patch-Based Segmentation. Medical Image Computing and Computer Assisted Interventions (MICCAI): 666-673, 2014.