Christian Ledig

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Publications

C. Ledig, A. Schuh, R. Guerrero, R. A. Heckemann and D. Rueckert, “Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database”, Scientific Reports, 8, 2018. [doi] [pdf] [dataset] [bib]

M. Deprez, S. Wang, C. Ledig, J. Hajnal, S. Counsell and J. Schnabel, “Segmentation of Myelin-like Signals on Clinical MR Images for Age Estimation in Preterm Infants”, bioRxiv:10.1101/357749, 2018. [pdf] [bib]

W. Shi, C. Ledig, Z. Wang, L. Theis and F. Huszar, “SUPER RESOLUTION USING A GENERATIVE ADVERSARIAL NETWORK”, US Patent Application: 15706428, 2018. [link]

A. Tolonen, H. F. M. Rhodius-Meester, M. Bruun, J. Koikkalainen, F. Barkhof, A. W. Lemstra, T. Koene, P. Scheltens, C. E. Teunissen, T. Tong, R. Guerrero, A. Schuh, C. Ledig, M. Baroni, D. Rueckert, H. Soininen, A. M. Remes, G. Waldemar, S. G. Hasselbalch, P. Mecocci, W. M. van der Flier and J. Lötjönen, “Data-driven differential diagnosis of dementia using multiclass Disease State Index classifier”, Frontiers in Aging Neuroscience, 10, article: 111, 2018. [doi] [pdf] [bib]

C. Ledig, K. Kamnitsas, J. Koikkalainen, J. P. Posti, R. S. K. Takala, A. Katila, J. Frantzén, H. Ala-Seppää, A. Kyllönen, H.-R. Maanpää, J. Tallus, J. Lötjönen, B. Glocker, O. Tenovuo and D. Rueckert, “Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging”, PLoS ONE, 12(11), pp. 1-31, 2017. [doi] [pdf] [bib]

A. I. R. Maas, D. K. Menon, P. D. Adelson, et al., “Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research”, The Lancet Neurology, 16(12), pp. 987-1048, 2017. [doi] [bib]

T. Tong, C. Ledig, R. Guerrero, A. Schuh, J. Koikkalainen, A. Tolonen, H. Rhodius, F. Barkhof, B. Tijms, A. W. Lemstra, H. Soininen, A. M. Remes, G. Waldemar, S. Hasselbalch, P. Mecocci, M. Baroni, J. Lötjönen, W. van der Flier and D. Rueckert, “Five-class Differential Diagnostics of Neurodegenerative Diseases using Random Undersampling Boosting”, accepted NeuroImage: Clinical, 2017. [doi] [pdf] [bib]

K. Kamnitsas, C. Baumgartner, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, A. Nori, A. Criminisi, D. Rueckert and B. Glocker, “Unsupervised domain adaptation in brain lesion segmentation with adversarial networks”, accepted at Information Processing in Medical Imaging IPMI, 2017. [pdf] [bib]

J. Caballero, C. Ledig, A. Aitken, A. Acosta, J. Totz, Z. Wang and W. Shi, “Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation”, accepted at CVPR, 2017. [pdf] [bib]

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, accepted at CVPR (oral), 2017. [pdf] [bib]

K. Kamnitsas, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert and B. Glocker, “Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation”, Medical Image Analysis, vol. 36, pp. 61-78, 2017. [pdf] [doi] [bib] [github]

K. Kamnitsas, E. Ferrante, S. Parisot, C. Ledig, A. Nori, A. Criminisi, D. Rueckert, B. Glocker, “ DeepMedic on Brain Tumor Segmentation” Proceedings of BRATS-MICCAI, pp. 18-22, 2016 [pdf] [doi] [bib]

C. Bowles, C. Qin, C. Ledig, R. Guerrero, R. Gunn, A. Hammers, E. Sakka, D. A. Dickie, M. Valdés Hernández, N. Royle, J. Wardlaw, H. Rhodius-Meester, B. Tijms, A. W. Lemstra, W. van der Flier, F. Barkhof, P. Scheltens and D. Rueckert, “Pseudo-healthy Image Synthesis for White Matter Lesion Segmentation”, International Workshop on Simulation and Synthesis in Medical Imaging (SASHIMI), pp. 87-96, 2016. [doi] [bib]

C. Qin, R. Guerrero, C. Bowles, C. Ledig, P. Scheltens, F. Barkhof, H. Rhodius-Meester, B. Tijms, A. W. Lemstra, W. van der Flier, B. Glocker and R. Daniel, “A Semi-supervised Large Margin Algorithm for White Matter Hyperintensity Segmentation”, Machine Learning in Medical Imaging: 7th International Workshop (MLMI), pp. 104-112, 2016. [doi] [bib]

R. Guerrero, C. Ledig, A. Schmidt-Richberg, D. Rueckert, “Group-constrained manifold learning: Application to AD risk assessment”, Pattern Recognition, vol. 63, pp. 570-582, 2017. [doi] [bib]

W. Shi, J. Caballero, L. Theis, F. Huszar, A. Aitken, C. Ledig, Z. Wang, “Is the deconvolution layer the same as a convolutional layer?”, arXiv:1609.07009, 2016. [pdf] [bib]

O. Maier, B. H. Menze, J. von der Gablentz, L. Häni, M. P. Heinrich, M. Liebrand, S. Winzeck, A. Basit, P. Bentley, L. Chen, D. Christiaens, F. Dutil, et al., “ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI”, Medical Image Analysis, vol. 35, pp. 250-269, 2016. [doi] [bib]

R. Guerrero, A. Schmidt-Richberg, C. Ledig, T. Tong, R. Wolz and D. Rueckert, “Instantiated mixed effects modeling of Alzheimer's disease markers”, NeuroImage, vol. 142, pp. 113-125, 2016. [doi] [bib]

C. Ledig, S. Kaltwang, A. Tolonen, J. Koikkalainen, P. Scheltens, F. Barkhof, H. Rhodius-Meester, B. Tijms, A. W. Lemstra, W. Van der Flier, J. Lötjönen, D. Rueckert, “Differential Dementia Diagnosis on Incomplete Data with Latent Trees”, Accepted in Medical Image Computing and Computer-Assisted Intervention MICCAI, 2016. [pdf] [bib]

P. Snape, S. Pszczolkowski, S. Zafeiriou, G. Tzimiropoulos, C. Ledig and D. Rueckert, “A Robust Similarity Measure for Volumetric Image Registration with Outliers”, Image and Vision Computing, vol. 52, pp. 97-113, 2016. [doi] [pdf] [bib]

A. Schmidt-Richberg, C. Ledig, R. Guerrero, H. Molina-Abril, A. Frangi and D. Rueckert, “Learning biomarker models for progression estimation of Alzheimer's disease”, PLoS ONE, 11(4), pp. e0153040, 2016. [doi] [pdf] [bib]

T. Tong, Q. Gao, R. Guerrero, C. Ledig, L. Chen and D. Rueckert, “A Novel Grading Biomarker for the Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease”, IEEE Transactions on Biomedical Engineering, vol. 64, pp. 155-165, 2016. [doi] [bib]

J. Lötjönen, A. Tolonen, H. Rhodius-Meester, M. Bruun, J. Koikkalainen, F. Barkhof, B. M. Tijms, A. W. Lemstra, T. Koene, C. E. Teunissen, T. Tong, R. Guerrero, A. Schuh, C. Ledig, M. Baroni, D. Rueckert, H. Soininen, A. Remes, G. Waldemar, S. G. Hasselbalch, P. Mecocci, W. M. van der Flier,“Towards Data-Driven Medicine in Differential Diagnostics of Neurodegenerative Diseases”, Abstract accepted at AAIC, 2016.

S. Wang, M. Murgasova, J. V. Hajnal, C. Ledig, J. Schnabel, “Regression Analysis for Assessment of Myelination Status in Preterm Brains with Magnetic Resonance Imaging”, Proceedings of ISBI, pp. 278-281, 2016. [doi] [bib]

J. Koikkalainen, H. Rhodius-Meester, A. Tolonen, F. Barkhof, B. Tijms, A. W. Lemstra, T. Tong, R. Guerrero, A. Schuh, C. Ledig, D. Rueckert, H. Soininen, A. M. Remes, G. Waldemar, S. Hasselbalch, P. Mecocci, W. van der Flier and J. Lötjönen, “Differential diagnosis of neurodegenerative diseases using structural MRI data”, NeuroImage: Clinical, vol. 11, pp. 435-449, 2016. [pdf] [doi] [bib]

C. Ledig (PhD Thesis), “Robust multi-structure segmentation of magnetic resonance brain images”, Advisor: Daniel Rueckert, PhD Thesis, Imperial College London, 2015. [doi] [pdf]

C. Ledig and D. Rueckert, “Semantic Parsing of Brain MR Images”, In: Zhou, K. ed. Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches (1st Edition), Academic Press, pp. 307-336, 2015. [bib] [doi]

R. Guerrero, C. Ledig, A. Schmidt-Richberg and D. Rueckert, “Group-Constraint Laplacian Eigenmaps: Longitudinal AD Biomarker Learning”, Machine Learning in Medical Imaging, vol. 9352, pp. 178-185, 2015. [bib] [doi]

K. Kamnitsas, L. Chen, C. Ledig, D. Rueckert and B. Glocker, “Multi-Scale 3D Convolutional Neural Networks for Lesion Segmentation in Brain MRI”, MICCAI Workshop - Ischemic Stroke Lesion Segmentation ISLES, pp. 13-16, 2015, (1st Place). [pdf] [bib] [results]

K. Kamnitsas, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert and B. Glocker, “Segmentation of Traumatic Brain Injuries with Convolutional Neural Networks”, 2nd Turku Traumatic Brain Injury Symposium TTBIS, 2015. [pdf] [bib]

A. Schmidt-Richberg, R. Guerrero, C. Ledig, H. Molina-Abril, A. F. Frangi, and D. Rueckert, “Multi-stage biomarker models for progression estimation in Alzheimer's disease”, in Information Processing in Medical Imaging IPMI, vol. 9123 of Lecture Notes in Computer Science, pp. 387-398, 2015. [doi][bib]

R. Heckemann, C. Ledig, K. R. Gray, P. Aljabar, D. Rueckert, J. V. Hajnal, and A. Hammers, “Brain extraction using label propagation and group agreement: pincram”, PLoS ONE, 10(7), pp. e0129211, 2015. [doi][pdf][bib]

R. Guerrero, A. Schmidt-Richberg, C. Ledig, D. Rueckert, “Alzheimer's disease progress estimation based on mixed-effect biomarker modelling”, Alzheimer's & Dementia, 11(7 Supplement), P798-P799, 2015. [pdf] [doi] [bib]

V. F. J. Newcombe, M. M. Correia, C. Ledig, M. G. Abate, J. G. Outtrim, D. Chatfield, T. Geeraerts, A. E. Manktelow, E. Garyfallidis, J. D. Pickard, B. J. Sahakian, P. J. A. Hutchinson, D. Rueckert and J. P. Coles, G. B. Williams and D. K. Menon, “Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI: A Diffusion Tensor Imaging Study”, Neurorehabilitation and Neural Repair, 2015. [doi][bib]

C. Ledig, R. Wolz, M. Austin, K. R. Gray, D. Rueckert, D. L. G. Hill, “Improved AD disease classification using structural volumes of the whole brain”, 12th International Conference on Alzheimer's and Parkinson's Diseases and Related Neurological Disorders (AD/PD), 2015. [pdf]

E. E. Bron, M. Smits, W. M. van der Flier, et al., “Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge”, Neuroimage, vol. 111, pp. 562-579, 2015. [doi] [bib]

C. Ledig, R. A. Heckemann, A. Hammers, J. C. Lopez, V. F. J. Newcombe, A. Makropoulos, J. Lötjönen, D. Menon and D. Rueckert, “Robust whole-brain segmentation: application to traumatic brain injury”, Medical Image Analysis, 21(1), pp. 40-58, 2015. [pdf] [doi] [bib]

W. Bai, W. Shi, C. Ledig, and D. Rueckert, “ Multi-atlas segmentation with augmented features for cardiac MR images”, Medical Image Analysis, 19(1), pp. 98-109, 2015. [pdf] [bib] [doi]

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”, accepted at MICCAI Workshop STIA, 2014. [doi] [bib]

R. Guerrero, C. Ledig, and D. Rueckert, “Manifold alignment and transfer learning for classification of Alzheimer's disease”, Machine Learning in Medical Imaging, vol. 8679, pp. 77-84, 2014, (Best Paper Award). [bib] [doi]

C. Ledig, R. Guerrero, T. Tong, K. Gray, A. Schmidt-Richberg, A. Makropoulos, R. A. Heckemann, and D. Rueckert, “Alzheimer's disease state classification using structural volumetry, cortical thickness and intensity features”, MICCAI workshop Challenge on Computer-Aided Diagnosis of Dementia based on structural MRI data. , pp. 55-64, 2014, (3rd Place). [bib] [pdf] [results]

W. Shi, H. Lombaert,W. Bai, C. Ledig, X. Zhuang, A. Marvao, T. Dawes, D. O'Regan, and D. Rueckert, “Multi-atlas Spectral PatchMatch: Application to cardiac image segmentation”, in Medical Image Computing and Computer-Assisted Intervention MICCAI 2014, vol. 8673 of Lecture Notes in Computer Science, pp. 348-355, 2014. [pdf] [bib] [doi]

C. Ledig, W. Shi, W. Bai, and D. Rueckert, “Patch-based evaluation of image segmentation”, CVPR, pp. 3065-3072, 2014. [bib] [pdf] [doi] [spotlight:mpeg4][spotlight:mov] [download]

C. Ledig, V. Newcombe, M. G. Abate, J. G. Outtrim, D. Chatfield, T. Geeraerts, A. Manktelow, P. J. Hutchinson, J. P. Coles, G.Williams, D. Rueckert, and D. Menon, “Dynamic evolution of atrophy after traumatic brain injury”, Abstract accepted at ISMRM, 2014. [bib][pdf]

R. Wright, V. Kyriakopoulou, C. Ledig, M. Rutherford, J. V. Hajnal, D. Rueckert, and P. Aljabar, “Automatic quantification of normal cortical folding patterns from foetal brain MRI”, NeuroImage, vol. 91, 21-32, 2014. [bib] [doi] [pdf]

A. Makropoulos, I. S. Gousias, C. Ledig, P. Aljabar, A. Serag, J. V. Hajnal, D. A. Edwards, S. J. Counsell, and D. Rueckert, “Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain”, IEEE Transactions on Medical Imaging, 33(9), pp. 1818-1831, 2014. [bib] [doi]

J. Lötjönen, C. Ledig, J. Koikkalainen, R. Wolz, L. Thurfjell, H. Soininen, S. Ourselin, and D. Rueckert, “Extented Boundary Shift Integral”, Proceedings of ISBI 2014, pp. 854-857, 2014. [bib] [doi]

C. Ledig, W. Shi, A. Makropoulos, J. Koikkalainen, R. A. Heckemann, A. Hammers, J. Lötjönen, and D. Rueckert, “Consistent and robust 4D whole-brain segmentation: application to traumatic brain injury”, Proceedings of ISBI 2014, pp. 673-676, 2014. [pdf] [bib] [doi]

A. Rao, C. Ledig, V. Newcombe, D. Menon, and D. Rueckert, “Contusion Segmentation from subjects with traumatic brain injury: a random forest framework”, Proceedings of ISBI 2014, pp. 333-336, 2014. [bib] [doi]

S. Pszczolkowski, S. Zafeirou, C. Ledig, and D. Rueckert, “A Robust Similarity Measure for Nonrigid Image Registration with Outliers”, Proceedings of ISBI 2014, pp. 868-871, 2014. [bib] [doi] [pdf]

J. Koikkalainen, J. Lötjönen, C. Ledig, D. Rueckert, O. Tenovuo, and D. Menon, “ Automatic Analysis of CT images for traumatic brain injury”, Proceedings of ISBI 2014, pp. 125-128, 2014. [bib] [doi]

O. Tenovuo, D. Menon, M. van Gils, D. Rueckert, A. Katila, J. P. Coles, J. Mattila, C. Ledig, J. Frantzen, J. G. Outtrim, and J. Lötjönen, “Improving the Individual Diagnostics of TBI the International TBIcare project”, Proceedings of Tenth World Congress on Brain Injury, 2014. [bib][pdf]

V. Newcombe, C. Ledig, G. Abate, J. Outtrim, D. Chatfield, T. Geeraerts, A. Manktelow, P. J. Hutchinson, J. Coles, G. Williams, D. Rueckert, and D. K. Menon, “Dynamic Evolution of Atrophy after moderate to severe Traumatic Brain Injury”, Journal of Neurotrauma, vol. 31, no. 5, pp. A36-A37, 2014. [bib] [doi]

C. Ledig, R. A. Heckemann, A. Hammers, and D. Rueckert, “Improving whole-brain segmentations through incorporating regional image intensity statistics”, Proceedings of SPIE 8669, Medical Imaging 2013, pp. 86691M-86691M-7, 2013. [pdf] [bib] [doi]

F. Deligianni, G. Varoquaux, B. Thirion, D. J. Sharp, C. Ledig, R. Leech, and D. Rueckert, “A framework for inter-subject prediction of functional connectivity from structural networks”, IEEE Transactions on Medical Imaging, vol. 32, no. 12, pp. 2200-2214, 2013. [pdf] [bib] [doi]

W. Shi, J. Caballero, C. Ledig, X. Zhuang, W. Bai, K. Bhatia, A. Marvao, T Dawes, D. ORegan, and D. Rueckert, “Cardiac image super-resolution with global correspondence using multi-atlas patchmatch”, in Medical Image Computing and Computer-Assisted Intervention MICCAI 2013, vol. 8151 of Lecture Notes in Computer Science, pp. 9-16. 2013. [pdf] [bib]

R. A. Heckemann, R. Husson, C. Ledig, D. Rueckert, and A. Hammers, “Positional normalization as a first step in processing magnetic resonance brain images: work in progress”, Swedish Society for Automated Image Analysis (SSBA), 2013. [pdf] [bib]

C. Ledig, R. Wolz, P. Aljabar, J. Lötjönen, R. A. Heckemann, A. Hammers, and D. Rueckert, “Multi-class brain segmentation using atlas propagation and EM-based refinement”, Proceedings of ISBI 2012, pp. 896-899, 2012. [pdf] [bib] [doi]

C. Ledig, R. A. Heckemann, P. Aljabar, R. Wolz, J. V. Hajnal, A. Hammers, and D. Rueckert, “Segmentation of MRI brain scans using MALP-EM”, MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling, pp. 79-82, 2012, (3rd Place). [pdf] [bib]

C. Ledig, R. Wolz, P. Aljabar, J. Lötjönen, and D. Rueckert, “PBSI: A probabilistic extension of the boundary shift integral”, 2012, pp. 125-132, MICCAI NIBAD, 2012. [pdf] [bib]

C. Ledig, R. Wright, A. Serag, P. Aljabar, and D. Rueckert, “Neonatal brain segmentation using second order neighbourhood information”, MICCAI PAPI, pp. 33-40, 2012. [pdf] [bib]

R. A. Heckemann, C. Ledig, P. Aljabar, K. R. Gray, D. Rueckert, J. V. Hajnal, and A. Hammers, “Label propagation using group agreement DISPATCH”, pp. 75-78, MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling, 2012. [pdf] [bib]

R. A. Heckemann, S. Keihaninejad, C. Ledig, P. Aljabar, D. Rueckert, J. V. Hajnal, and A. Hammers, “Multi-atlas propagation with enhanced registration - MAPER”, pp. 83- 86, MICCAI 2012 Grand Challenge and Workshop on Multi- Atlas Labeling, 2012. [pdf] [bib]

J. Lötjönen, R. Wolz, J. Koikkalainen, V. Manna, C. Ledig, L. Thurfjell, R. Lundqvist, G. Waldemar, H. Soininen, and D. Rueckert, “Hippocampal atrophy rate using an expectation maximization classifier with a disease-specific prior”, Proceedings of ISBI 2012, pp. 1164-1167, 2012. [bib] [doi]

R. Wright, D. Vatansever, V. Kyriakopoulou, C. Ledig, R.Wolz, A. Serag, D. Rueckert, M. A. Rutherford, J. V. Hajnal, and P. Aljabar, “Age dependent fetal MR segmentation using manual and automated approaches”, MICCAI PAPI, pp. 97-104, 2012. [pdf] [bib]

A. Makropoulos, C. Ledig, P. Aljabar, A. Serag, J. V. Hajnal, D. A. Edwards, J. C. Serena, and D. Rueckert, “Automatic tissue and structural segmentation of neonatal brain MRI using Expectation-Maximization”, MICCAI Grand Challenge: Neonatal Brain Segmentation 2012 (NeoBrainS12), 2012, (1st Place). [pdf] [bib]

A. Makropoulos, I. S. Gousias, C. Ledig, P. Aljabar, A. Serage, J. V. Hajnal, D. A. Edwards, S. J. Counsell, and D. Rueckert, “Automatic multi-label segmentation of the preterm brain with the use of adaptive atlases”, Abstract accepted at ISMRM, 2012. [bib]

C. Ledig (Diploma Thesis), “Efficient Implementation of Nonrigid Registration Methods on commodity Hardware with Cuda”, Advisors: C. Chefd'hotel, D. Hahn, J. Hornegger, G. Leugering, Diploma Thesis, Siemens Corporate Reserach, Princeton, U.S.A. and Friedrich Alexander Universität Erlangen-Nuremberg, Germany, 2010.

C. Ledig,and C. Chefd'Hotel, “Efficient computation of joint histograms and normalized mutual information on CUDA compatible devices”, HP-MICCAI, pp. 90-99, 2010. [pdf] [bib]