2023

Dorent, R., Kujawa, A., Ivory, M., Bakas, S., Rieke, N., Joutard, S., ...Batmanghelich, K. (2023). CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation. MEDICAL IMAGE ANALYSIS, 83 doi:10.1016/j.media.2022.102628

2022

Burnhope, E., Waring, M., Guilder, A., Malhotra, B., Cardoso, J.M., Razavi, R., Carr-White, G. (2022). A systematic approach towards implementing value-based health care in heart failure: Understandings from retrospective analysis methods in South London. Health Services Management Research, 35 (1), 37-47. doi:10.1177/0951484820971442
Sudre, C.H., Moriconi, S., Rehwald, R., Smith, L., Tillin, T., Barnes, J., ...Hughes, A.D. (2022). Accelerated vascular aging: Ethnic differences in basilar artery length and diameter, and its association with cardiovascular risk factors and cerebral small vessel disease. Frontiers in Cardiovascular Medicine, 9 doi:10.3389/fcvm.2022.939680
Nderitu, P., do Rio, J.M.N., Webster, M.L., Mann, S.S., Hopkins, D., Cardoso, M.J., ...Jackson, T.L. (2022). Automated image curation in diabetic retinopathy screening using deep learning. SCIENTIFIC REPORTS, 12 (1), doi:10.1038/s41598-022-15491-1
Pinaya, W.H.L., Tudosiu, P.D., Dafflon, J., Da Costa, P.F., Fernandez, V., Nachev, P., ...Cardoso, M.J. (2022). Brain Imaging Generation with Latent Diffusion Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13609 117-126. doi:10.1007/978-3-031-18576-2_12
Fernandez, V., Pinaya, W.H.L., Borges, P., Tudosiu, P.-.D., Graham, M.S., Vercauteren, T., Cardoso, M.J. (2022). Can Segmentation Models Be Trained with Fully Synthetically Generated Data?. In Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (Eds.), (pp. 79-90). SPRINGER INTERNATIONAL PUBLISHING AG.
Patel, A., Tudosiu, P.D., Pinaya, W.H.L., Cook, G., Goh, V., Ourselin, S., Cardoso, M.J. (2022). Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection. In (pp. 14-23). .
Nelson, A.P.K., Gray, R.J., Ruffle, J.K., Watkins, H.C., Herron, D., Sorros, N., ...McNally, N. (2022). Deep forecasting of translational impact in medical research. Patterns, doi:10.1016/j.patter.2022.100483
Diaz-Pinto, A., Mehta, P., Alle, S., Asad, M., Brown, R., Nath, V., ...Pinter, C. (2022). DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images.
Pinaya, W.H.L., Graham, M.S., Gray, R., da Costa, P.F., Tudosiu, P.D., Wright, P., ...Jager, R. (2022). Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models.
Brudfors, M., Balbastre, Y., Ashburner, J., Rees, G., Nachev, P., Ourselin, S., Cardoso, M.J. (2022). Fitting Segmentation Networks on Varying Image Resolutions using Splatting.
Cardoso, M.J., Li, W., Brown, R., Ma, N., Kerfoot, E., Wang, Y., ...Yang, D. (2022). MONAI: An open-source framework for deep learning in healthcare.
Tudosiu, P.-.D., Pinaya, W.H.L., Graham, M.S., Borges, P., Fernandez, V., Yang, D., ...Vella, M. (2022). Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain.
Cardoso, M.J., Dou, Q., Islam, M., Kamnitsas, K., Koch, L., Rieke, N., ...Xu, Z. (2022). Preface. .
Albarqouni, S., Bakas, S., Cardoso, M.J., Landman, B., Li, X., Qin, C., ...Xu, D. (2022). Preface DeCaF 2022. .
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B.A., ...Summers, R.M. (2022). The Medical Segmentation Decathlon. NATURE COMMUNICATIONS, 13 (1), doi:10.1038/s41467-022-30695-9
Pinaya, W.H.L., Tudosiu, P.-.D., Gray, R., Rees, G., Nachev, P., Ourselin, S., Cardoso, M.J. (2022). Unsupervised brain imaging 3D anomaly detection and segmentation with transformers. Medical Image Analysis, 79 doi:10.1016/j.media.2022.102475

2021

Klaser, K., Borges, P., Shaw, R., Ranzini, M., Modat, M., Atkinson, D., ...Cook, G. (2021). A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis. Applied Sciences, 11 (4), 1667. doi:10.3390/app11041667
James, S.-.N., Nicholas, J.M., Lane, C.A., Parker, T.D., Lu, K., Keshavan, A., ...Wong, A. (2021). A population-based study of head injury, cognitive function and pathological markers.. Ann Clin Transl Neurol, doi:10.1002/acn3.51331
Booth, T.C., Thompson, G., Bulbeck, H., Boele, F., Buckley, C., Cardoso, J., ...Kreindler, J. (2021). A Position Statement on the Utility of Interval Imaging in Standard of Care Brain Tumour Management: Defining the Evidence Gap and Opportunities for Future Research. FRONTIERS IN ONCOLOGY, 11 doi:10.3389/fonc.2021.620070
Brudfors, M., Balbastre, Y., Ashburner, J., Rees, G., Nachev, P., Ourselin, S., Cardoso, M.J. (2021). An MRF-UNet Product of Experts for Image Segmentation.
Burgos, N., Cardoso, M.J., Samper-González, J., Habert, M.O., Durrleman, S., Ourselin, S., Colliot, O. (2021). Anomaly detection for the individual analysis of brain PET images. Journal of Medical Imaging, 8 (2), doi:10.1117/1.JMI.8.2.024003
Sudre, C.H., Murray, B., Varsavsky, T., Graham, M.S., Penfold, R.S., Bowyer, R.C., ...Canas, L.S. (2021). Attributes and predictors of long COVID. Nature Medicine, doi:10.1038/s41591-021-01292-y
Sudre, C.H., Murray, B., Varsavsky, T., Graham, M.S., Penfold, R.S., Bowyer, R.C., ...Canas, L.S. (2021). Attributes and predictors of long COVID (vol 27, pg 626, 2021). NATURE MEDICINE, 27 (6), 1116. doi:10.1038/s41591-021-01361-2
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Saiz, L.A., Cardoso, M.J., ...Kopp-Schneider, A. (2021). Author Correction: Methods and open-source toolkit for analyzing and visualizing challenge results (Scientific Reports, (2021), 11, 1, (2369), 10.1038/s41598-021-82017-6). Scientific Reports, 11 (1), doi:10.1038/s41598-021-88636-3
Pemberton, H.G., Goodkin, O., Prados, F., Das, R.K., Vos, S.B., Moggridge, J., ...Schmitt, A. (2021). Automated quantitative MRI volumetry reports support diagnostic interpretation in dementia: a multi-rater, clinical accuracy study.. Eur Radiol, doi:10.1007/s00330-020-07455-8
Albarqouni, S., Bakas, S., Cardoso, M.J., Landman, B., Li, X., Rieke, N., ...Xu, D. (2021). DCL preface. .
Varsavsky, T., Graham, M.S., Canas, L.S., Ganesh, S., Capdevila Pujol, J., Sudre, C.H., ...Astley, C.M. (2021). Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study. The Lancet Public Health, 6 (1), e21-e29. doi:10.1016/S2468-2667(20)30269-3
Silva, M.D., Sudre, C.H., Garcia, K., Bass, C., Cardoso, M.J., Robinson, E.C. (2021). Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks.
Goodkin, O., Prados, F., Vos, S.B., Pemberton, H., Collorone, S., Hagens, M.H.J., ...Sudre, C.H. (2021). FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis. NeuroImage: Clinical, 102542. doi:10.1016/j.nicl.2020.102542
Bowyer, R.C.E., Varsavsky, T., Thompson, E.J., Sudre, C.H., Murray, B.A.K., Freidin, M.B., ...Bakker, E. (2021). Geo-social gradients in predicted COVID-19 prevalence in Great Britain: Results from 1 960 242 users of the COVID-19 Symptoms Study app. Thorax, doi:10.1136/thoraxjnl-2020-215119
Kläser, K., Varsavsky, T., Markiewicz, P., Vercauteren, T., Hammers, A., Atkinson, D., ...Ourselin, S. (2021). Imitation learning for improved 3D PET/MR attenuation correction. Medical Image Analysis, 71 doi:10.1016/j.media.2021.102079
Dorent, R., Booth, T., Li, W., Sudre, C.H., Kafiabadi, S., Cardoso, J., ...Vercauteren, T. (2021). Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets. Medical Image Analysis, 101862. doi:10.1016/j.media.2020.101862
Groothuis, I., Sudre, C.H., Ingala, S., Barnes, J., Gispert, J.D., Sørensen, L., ...Cardoso, M.J. (2021). Lesion-wise evaluation for effective performance monitoring of small object segmentation.
Siakallis, L., Sudre, C.H., Mulholland, P., Fersht, N., Rees, J., Topff, L., ...Panovska-Griffiths, J. (2021). Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance. Neuroradiology: a journal devoted to neuroimaging and interventional neuroradiology, doi:10.1007/s00234-021-02719-6
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Saiz, L.A., Cardoso, M.J., ...Kopp-Schneider, A. (2021). Methods and open-source toolkit for analyzing and visualizing challenge results. SCIENTIFIC REPORTS, 11 (1), doi:10.1038/s41598-021-82017-6
Vrenken, H., Jenkinson, M., Pham, D., Guttmann, C.R.G., Pareto, D., Paardekooper, M., ...Cardoso, M.J. (2021). Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence. Neurology, doi:10.1212/WNL.0000000000012884
Albarqouni, S., Cardoso, M.J., Dou, Q., Kamnitsas, K., Rieke, N., Tsaftaris, S., ...Xu, Z. (2021). Preface dart 2021. .
Fiford, C.M., Sudre, C.H., Young, A.L., Macdougall, A., Nicholas, J., Manning, E.N., ...Pemberton, H.G. (2021). Presumed small vessel disease, imaging and cognition markers in the Alzheimer's Disease Neuroimaging Initiative. Brain Communications, 3 (4), doi:10.1093/braincomms/fcab226
Bonkhoff, A.K., Xu, T., Nelson, A., Gray, R., Jha, A., Cardoso, J., ...Nachev, P. (2021). Reclassifying stroke lesion anatomy. Cortex, doi:10.1016/j.cortex.2021.09.007
Leon-Rojas, J.E., Iqbal, S., Vos, S.B., Rodionov, R., Miserocchi, A., McEvoy, A.W., ...Sparks, R.E. (2021). Resection of the piriform cortex for temporal lobe epilepsy: a Novel approach on imaging segmentation and surgical application. British Journal of Neurosurgery, doi:10.1080/02688697.2021.1966385
Ghazi, M.M., Nielsen, M., Pai, A., Modat, M., Cardoso, M.J., Ourselin, S., Sorensen, L. (2021). Robust parametric modeling of Alzheimer's disease progression. NEUROIMAGE, 225 doi:10.1016/j.neuroimage.2020.117460
Sudre, C.H., Lee, K.A., Lochlainn, M.N., Varsavsky, T., Murray, B., Graham, M.S., ...Nguyen, L.H. (2021). Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app.. Sci Adv, 7 (12), doi:10.1126/sciadv.abd4177
Dos Santos Simoes Alves Borges, P., Shaw, R., Varsavsky, T., Klaser, K., Thomas, D., Drobnjak, I., ...Cardoso, M. (2021). The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructive Uncertainties.
Markiewicz, P., Matthews, J.C., Ashburner, J., Cash, D., Thomas, D., De Vita, E., ...Brown, R. (2021). Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging. NeuroImage, doi:10.1016/j.neuroimage.2021.117821

2020

Chung, K.K., Altmann, D., Barkhof, F., Miszkiel, K., Brex, P.A., O'Riordan, J., ...Vercauteren, T. (2020). A 30‐Year Clinical and Magnetic Resonance Imaging Observational Study of Multiple Sclerosis and Clinically Isolated Syndromes. Annals of Neurology, 87 (1), 63-74. doi:10.1002/ana.25637
Shaw, R., Sudre, C.H., Ourselin, S., Cardoso, M.J. (2020). A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality.
Shaw, R., Sudre, C.H., Varsavsky Aisemberg, T., Ourselin, S., Cardoso, M.J. (2020). A k-Space Model of Movement Artefacts: Application to Segmentation Augmentation and Artefact Removal. IEEE Transactions on Medical Imaging,
Sudre, C., Murray, B., Varsavsky, T., Graham, M., Penfold, R., Bowyer, R., ...Canas, L. (2020). Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App. Cold Spring Harbor, NY, USA: MedRxiv.
Ranzini, M.B.M., Henckel, J., Ebner, M., Cardoso, M.J., Isaac, A., Vercauteren, T., ...Modat, M. (2020). Automated postoperative muscle assessment of hip arthroplasty patients using multimodal imaging joint segmentation. Computer Methods and Programs in Biomedicine, 183 105062. doi:10.1016/j.cmpb.2019.105062
Fiford, C., Sudre, C., Pemberton, H., Walsh, P., Manning, E., Malone, I., ...Biessels, G.J. (2020). Automated white matter hyperintensity segmentation using Bayesian Model Selection: assessment and correlations with cognitive change. NeuroInformatics, doi:10.1007/s12021-019-09439-6
Convery, R.S., Neason, M., Cash, D.M., Cardoso, M.J., Modat, M., Ourselin, S., ...Bocchetta, M. (2020). Basal forebrain atrophy in frontotemporal dementia. NeuroImage: Clinical, 102210. doi:10.1016/j.nicl.2020.102210
Silva, M.D., Garcia, K., Sudre, C.H., Bass, C., Cardoso, M.J., Robinson, E. (2020). Biomechanical modelling of brain atrophy through deep learning.
Pålhaugen, L., Sudre, C.H., Tecelao, S., Nakling, A., Almdahl, I.S., Kalheim, L.F., ...Aarsland, D. (2020). Brain amyloid and vascular risk are related to distinct white matter hyperintensity patterns. Journal of Cerebral Blood Flow and Metabolism, doi:10.1177/0271678X20957604
Lee, K.A., Ma, W., Sikavi, D.R., Drew, D.A., Nguyen, L.H., Bowyer, R.C.E., ...Gomez, M. (2020). Cancer and Risk of COVID-19 Through a General Community Survey. Oncologist, doi:10.1634/theoncologist.2020-0572
Goodkin, O., Pemberton, H.G., Vos, S.B., Prados, F., Das, R.K., Moggridge, J., ...Campion, T. (2020). Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis.. Eur Radiol, doi:10.1007/s00330-020-07075-2
Ranzini, M.B.M., Groothuis, I., Klaser, K., Cardoso, M.J., Henckel, J., Ourselin, S., ...Modat, M. (2020). Combining Multimodal Information for Metal Artefact Reduction: An Unsupervised Deep Learning Framework.
Pissas, T., Bloch, E., Cardoso, M.J., Flores, B., Georgiadis, O., Jalali, S., ...Bergeles, C. (2020). Deep iterative vessel segmentation in OCT angiography. Biomedical Optics Express, 11 (5), 2490-2510. doi:10.1364/BOE.384919
Prados, F., Moccia, M., Johnson, A., Yiannakas, M., Grussu, F., Cardoso, M.J., ...Wheeler-Kingshott, C. (2020). Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy. NeuroImage, 209 doi:10.1016/j.neuroimage.2019.116489
Bowyer, R., Varsavsky, T., Sudre, C., Murray, B., Freidin, M., Yarand, D., ...Bakker, E. (2020). Geo-social gradients in predicted COVID-19 prevalence and severity in Great Britain: results from 2,266,235 users of the COVID-19 Symptoms Tracker app. .
Graham, M.S., Sudre, C.H., Varsavsky, T., Tudosiu, P.-.D., Nachev, P., Ourselin, S., Cardoso, M.J. (2020). Hierarchical brain parcellation with uncertainty.
Fiford, C., Nicholas, J., Biessels, G.J., Lane, C., Cardoso, M.J., Barnes, J. (2020). High blood pressure predicts hippocampal atrophy rate in cognitively impaired elders. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, doi:10.1002/dad2.12035
Singleton, E.H., Pijnenburg, Y.A.L., Sudre, C.H., Groot, C., Kochova, E., Barkhof, F., ...Miller, B. (2020). Investigating the clinico-anatomical dissociation in the behavioral variant of Alzheimer disease. Alzheimer's Research and Therapy, 12 (1), doi:10.1186/s13195-020-00717-z
Ravasio, C.S., Pissas, T., Bloch, E., Flores, B., Jalali, S., Stoyanov, D., ...Bergeles, C. (2020). Learned optical flow for intra-operative tracking of the retinal fundus. International Journal of Computer Assisted Radiology and Surgery, doi:10.1007/s11548-020-02160-9
Booth, T.C., Williams, M., Luis, A., Cardoso, J., Ashkan, K., Shuaib, H. (2020). Machine learning and glioma imaging biomarkers. CLINICAL RADIOLOGY, 75 (1), 20-32. doi:10.1016/j.crad.2019.07.001
Sudre, C.H., Panovska-Griffiths, J., Sanverdi, E., Brandner, S., Katsaros, V.K., Stranjalis, G., ...Avsenik, J. (2020). Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.. BMC Med Inform Decis Mak, 20 (1), 149. doi:10.1186/s12911-020-01163-5
Kanber, B., Ruffle, J., Cardoso, J., Ourselin, S., Ciccarelli, O. (2020). Neurosense: deep sensing of full or near-full coverage head/brain scans in human magnetic resonance imaging.. Neuroinformatics, doi:10.1007/s12021-019-09442-x
Lillington, J., Brusaferri, L., Klaser, K., Shmueli, K., Neji, R., Hutton, B., ...Ourselin, S. (2020). PET/MRI Attenuation estimation in the lung: A review of past, present, and potential techniques. Medical Physics, doi:10.1002/mp.13943
Albarqouni, S., Cardoso, M.J., Kamnitsas, K., Milletari, F., Rieke, N., Xu, D., Xu, Z. (2020). Preface dart 2020. .
Albarqouni, S., Bakas, S., Cardoso, M.J., Landman, B., Li, W., Milletari, F., ...Xu, D. (2020). Preface dcl 2020. .
Drew, D.A., Nguyen, L.H., Steves, C.J., Menni, C., Freydin, M., Varsavsky, T., ...Wolf, J. (2020). Rapid implementation of mobile technology for real-time epidemiology of COVID-19. SCIENCE, 368 (6497), 1362-+. doi:10.1126/science.abc0473
Menni, C., Valdes, A.M., Freidin, M.B., Sudre, C.H., Nguyen, L.H., Drew, D.A., ...Moustafa, J.S.E.-.S. (2020). Real-time tracking of self-reported symptoms to predict potential COVID-19. NATURE MEDICINE, 26 (7), 1037-+. doi:10.1038/s41591-020-0916-2
Nguyen, L.H., Drew, D.A., Graham, M.S., Joshi, A.D., Guo, C.G., Ma, W., ...Lo, C.H. (2020). Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. The Lancet Public Health, 5 (9), e475-e483. doi:10.1016/S2468-2667(20)30164-X
Nguyen, L.H., Drew, D.A., Joshi, A.D., Guo, C.-.G., Ma, W., Mehta, R.S., ...Song, M. (2020). Risk of COVID-19 among frontline healthcare workers and the general community: a prospective cohort study.. medRxiv, doi:10.1101/2020.04.29.20084111
Bragman, F.J.S., Tanno, R., Ourselin, S., Alexander, D.C., Cardoso, M.J. (2020). Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels.
Ma, D., Cardoso, M.J., Zuluaga, M.A., Modat, M., Powell, N.M., Wiseman, F.K., ...Siow, B. (2020). Substantially thinner internal granular layer and reduced molecular layer surface in the cerebellar cortex of the Tc1 mouse model of down syndrome – a comprehensive morphometric analysis with active staining contrast-enhanced MRI. NeuroImage, 117271. doi:10.1016/j.neuroimage.2020.117271
Varsavsky, T., Orbes-Arteaga, M., Sudre, C.H., Graham, M.S., Nachev, P., Cardoso, M.J. (2020). Test-time Unsupervised Domain Adaptation.
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H.R., Albarqouni, S., ...Maier-Hein, K. (2020). The future of digital health with federated learning. NPJ DIGITAL MEDICINE, 3 (1), doi:10.1038/s41746-020-00323-1
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H.R., Albarqouni, S., ...Maier-Hein, K. (2020). The future of digital health with federated learning.. NPJ digital medicine, 3 (1), 119-. doi:10.1038/s41746-020-00323-1
Klaser, K., Borges, P., Shaw, R., Ranzini, M., Modat, M., Atkinson, D., ...Cook, G. (2020). Uncertainty-aware multi-resolution whole-body MR to CT synthesis.

2019

Sudre, C.H., Anson, B.G., Ingala, S., Lane, C.D., Jimenez, D., Haider, L., ...Cardoso, M.J. (2019). 3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects. Proceedings of Machine Learning Research,
Simpson, A.L., Antonelli, M., Bakas, S., Bilello, M., Farahani, K., Ginneken, B.V., ...Menze, B. (2019). A large annotated medical image dataset for the development and evaluation of segmentation algorithms.
Eshaghi, A., Kievit, R.A., Prados Carrasco, F., Sudre, C., Nicholas, J., Cardoso, M.J., ...Greenwood, J. (2019). Applying causal models to explore the mechanism of action of simvastatin in progressive multiple sclerosis.. PNAS,
Eaton-Rosen, Z., Varsavsky, T., Ourselin, S., Cardoso, M.J. (2019). As Easy as 1, 2 ... 4? Uncertainty in Counting Tasks for Medical Imaging. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT IV, 11767 356-364. doi:10.1007/978-3-030-32251-9_39
Haddow, L.J., Godi, C., Sokolska, M., Cardoso, M.J., Oliver, R., Winston, A., ...Williams, I.G. (2019). Brain Perfusion, Regional Volumes, and Cognitive Function in Human Immunodeficiency Virus–positive Patients Treated With Protease Inhibitor Monotherapy. Clinical Infectious Diseases, doi:10.1093/cid/ciy617
Leijenaar, J.F., Groot, C., Sudre, C.H., Bergeron, D., Leeuwis, A.E., Cardoso, M.J., ...van der Flier, W.M. (2019). Comorbid amyloid-β pathology affects clinical and imaging features in VCD. Alzheimer's & Dementia, doi:10.1016/j.jalz.2019.08.190
Araújo, R.J., Garrido, V., Baraças, C.A., Vasconcelos, M.A., Mavioso, C., Anacleto, J.C., ...Oliveira, H.P. (2019). Computer Aided Detection of Deep Inferior Epigastric Perforators in Computed Tomography Angiography scans.
Smith, L., Melbourne, A., Owen, D., Cardoso, M., Sudre, C., Tillin, T., ...Ourselin, S. (2019). Cortical cerebral blood flow in ageing: effects of haematocrit, sex, ethnicity and diabetes. European Radiology, doi:10.1007/s00330-019-06096-w
Brownlee, W.J., Solanky, B., Prados, F., Yiannakas, M., Da Mota, P., Riemer, F., ...Gandini Wheeler-Kingshott, C. (2019). Cortical grey matter sodium accumulation is associated with disability and secondary progressive disease course in relapse-onset multiple sclerosis. Journal of Neurology, Neurosurgery and Psychiatry, doi:10.1136/jnnp-2018-319634
Kanber, B., Nachev, P., Barkhof, F., Calvi, A., Cardoso, J., Cortese, R., ...Ourselin, S. (2019). Erratum: Author Correction: High-dimensional detection of imaging response to treatment in multiple sclerosis. NPJ Digit Med, 2 66. doi:10.1038/s41746-019-0144-7
Antonelli, M., Cardoso, M.J., Johnston, E.W., Appayya, M.B., Presles, B., Modat, M., ...Ourselin, S. (2019). GAS: A genetic atlas selection strategy in multi-atlas segmentation framework. Medical Image Analysis, 52 97-108. doi:10.1016/j.media.2018.11.007
Kanber, B., Nachev, P., Barkhof, F., Calvi, A., Cardoso, M., Cortese, R., ...Ourselin, S. (2019). High-dimensional detection of imaging response to treatment in multiple sclerosis. npj Digital Medicine, doi:10.1038/s41746-019-0127-8
Vos, S.B., Winston, G.P., Goodkin, O., Pemberton, H.G., Barkhof, F., Prados, F., ...Cardoso, M.J. (2019). Hippocampal profiling: Localized magnetic resonance imaging volumetry and T2 relaxometry for hippocampal sclerosis.. Epilepsia, doi:10.1111/epi.16416
Sudre, C.H., Jorge Cardoso, M., Modat, M., Ourselin, S. (2019). Imaging biomarkers in alzheimer’s disease. In Handbook of Medical Image Computing and Computer Assisted Intervention. (pp. 343-378). .
Klaser, K., Varsavsky, T., Markiewicz, P., Atkinson, D., Thielemans, K., Hutton, B., ...Ourselin, S. (2019). Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning.
Orbes-Arteainst, M., Cardoso, J., Sorensen, L., Igel, C., Ourselin, S., Modat, M., ...Pai, A. (2019). Knowledge Distillation for Semi-supervised Domain Adaptation.
Bragman, F.J.S., Tanno, R., Ourselin, S., Alexander, D.C., Cardoso, M.J. (2019). Learning task-specific and shared representations in medical imaging.
Bennett, O.F., Kanber, B., Hoskote, C., Cardoso, M.J., Ourselin, S., Duncan, J.S., Winston, G.P. (2019). Learning to see the invisible: A data-driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy.. Epilepsia, doi:10.1111/epi.16380
Sudre, C.H., Anson, B.G., Ingala, S., Lane, C.D., Jimenez, D., Haider, L., ...Ourselin, S. (2019). Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling.. MICCAI (4), 11767 665-673. doi:10.1007/978-3-030-32251-9_73
Firth, N.C., Primativo, S., Marinescu, R.-.V., Shakespeare, T.J., Suarez-Gonzalez, A., Lehmann, M., ...Paterson, R.W. (2019). Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy.. Brain, doi:10.1093/brain/awz136
Haddow, L., Sudre, C.H., Sokolska, M., Gilson, R.C., Williams, I., Golay, X., ...Cardoso, J. (2019). Magnetic resonance imaging of cerebral small vessel disease in men living with HIV and HIV negative men aged 50 and above.. AIDS Res Hum Retroviruses, doi:10.1089/AID.2018.0249
Cardoso, M.J., Feragen, A., Glocker, B., Konukoglu, E., Oguz, I., Unal, G., Vercauteren, T. (2019). Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track.
Orbes-Arteaga, M., Varsavsky, T., Sudre, C.H., Eaton-Rosen, Z., Haddow, L.J., Sørensen, L., ...Modat, M. (2019). Multi-domain adaptation in brain MRI through paired consistency and adversarial learning.
Ghazi, M.M., Nielsen, M., Pai, A., Modat, M., Cardoso, M.J., Ourselin, S., Sorensen, L. (2019). On the Initialization of Long Short-Term Memory Networks.
Orbes-Arteaga, M., Sorensen, L., Cardoso, J., Modat, M., Ourselin, S., Sommer, S., ...Pai, A. (2019). PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation.
Brugulat-Serrat, A., Salvadó, G., Sudre, C.H., Grau-Rivera, O., Suárez-Calvet, M., Falcon, C., ...Cardoso, M.J. (2019). Patterns of white matter hyperintensities associated with cognition in middle-aged cognitively healthy individuals. Brain Imaging and Behavior, doi:10.1007/s11682-019-00151-2
Borges, P., Sudre, C., Varsavsky, T., Thomas, D., Drobnjak, I., Ourselin, S., Cardoso, M.J. (2019). Physics-informed brain MRI segmentation.
Milletari, F., Rieke, N., Albarqouni, S., Xu, Z., Kamnitsas, K., Cardoso, M.J. (2019). Preface. .
Canas, L.S., Sudre, C.H., De Vita, E., Nihat, A., Mok, T.H., Slattery, C.F., ...Cardoso, M.J. (2019). Prion disease diagnosis using subject-specific imaging biomarkers within a multi-kernel Gaussian process. NeuroImage: Clinical, 102051. doi:10.1016/j.nicl.2019.102051
Li, W., Milletari, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., ...Cardoso, M.J. (2019). Privacy-Preserving Federated Brain Tumour Segmentation.
Salvadó, G., Brugulat-Serrat, A., Sudre, C.H., Grau-Rivera, O., Suárez-Calvet, M., Falcon, C., ...Molinuevo, J.L. (2019). Spatial patterns of white matter hyperintensities associated with Alzheimer's disease risk factors in a cognitively healthy middle-aged cohort. Alzheimer's Research and Therapy, 11 (1), 12. doi:10.1186/s13195-018-0460-1
Kuijf, H.J., Biesbroek, J.M., de Bresser, J., Heinen, R., Andermatt, S., Bento, M., ...Casamitjana, A. (2019). Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge.. IEEE Trans Med Imaging, doi:10.1109/TMI.2019.2905770
Ma, D., Holmes, H.E., Cardoso, M.J., Modat, M., Harrison, I.F., Powell, N.M., ...O'Neill, M.J. (2019). Study the Longitudinal in vivo and Cross-Sectional ex vivo Brain Volume Difference for Disease Progression and Treatment Effect on Mouse Model of Tauopathy Using Automated MRI Structural Parcellation. FRONTIERS IN NEUROSCIENCE, 13 doi:10.3389/fnins.2019.00011
Goodkin, O., Pemberton, H., Vos, S.B., Prados, F., Sudre, C.H., Moggridge, J., ...White, M. (2019). The quantitative neuroradiology initiative framework: application to dementia. The British Institute of Radiology, 20190365. doi:10.1259/bjr.20190365
Moriconi, S., Rehwald, R., Zuluaga, M.A., Jäger, H.R., Nachev, P., Ourselin, S., Cardoso, M.J. (2019). Towards Quantifying Neurovascular Resilience. Lecture Notes in Computer Science, 149-157. doi:10.1007/978-3-030-33327-0_18
Ghazi, M.M., Nielsen, M., Pai, A., Cardoso, M.J., Modat, M., Ourselin, S., Sorensen, L. (2019). Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling. MEDICAL IMAGE ANALYSIS, 53 39-46. doi:10.1016/j.media.2019.01.004
Sudre, C.H., Bocchetta, M., Heller, C., Convery, R., Neason, M., Moore, K.M., ...Foiani, M. (2019). White matter hyperintensities in progranulin-associated frontotemporal dementia – a longitudinal GENFI study. NeuroImage: Clinical, 102077. doi:10.1016/j.nicl.2019.102077

2018

Berger, L., Eoin, H., Cardoso, M.J., Ourselin, S. (2018). An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation.
Sudre, C.H., Anson, B.G., Davagnanamd, I., Schmitt, A., Mendelson, A.F., Prados, F., ...Chaturvedi, N. (2018). Bullseye's representation of cerebral white matter hyperintensities. Journal of Neuroradiology, doi:10.1016/j.neurad.2017.10.001
Sudre, C.H., Smith, L., Atkinson, D., Chaturvedi, N., Ourselin, S., Barkhof, F., ...Cardoso, M.J. (2018). Cardiovascular Risk Factors and White Matter Hyperintensities: Difference in Susceptibility in South Asians Compared With Europeans. Journal of the American Heart Association, 7 (21), e010533. doi:10.1161/JAHA.118.010533
Groot, C., Sudre, C.H., Barkhof, F., Teunissen, C.E., van Berckel, B.N.M., Seo, S.W., ...van der Flier, W.M. (2018). Clinical phenotype, atrophy, and small vessel disease in APOEε2 carriers with Alzheimer disease.. Neurology, doi:10.1212/WNL.0000000000006503
Smith, L., Melbourne, A., Owen, D., Cardoso, M.J., Sudre, C., Tillin, T., ...Ourselin, S. (2018). Cortical Cerebral Blood Flow in Aging: Effects of Haematocrit, Sex and Ethnicity.
Klaser, K., Markiewicz, P.J., Ranzini, M., Li, W., Modat, M., Hutton, B., ...Ourselin, S. (2018). Deep boosted regression for MR to CT synthesis.
Eshaghi, A., Prados Carrasco, F., Brownlee, W., Altmann, D.R., Tur Gomez, C., Cardoso, M.J., ...de Stefano, N. (2018). Deep gray matter volume loss drives disability worsening in multiple sclerosis. Annals of Neurology, 83 (2), 210-222. doi:10.1002/ana.25145
Oguz, I., Landman, B., Cardoso, J.M. (2018). DLF 2018 preface. .
Moriconi, S., Zuluaga, M.A., Jäger, H.R., Nachev, P., Ourselin, S., Cardoso, M.J. (2018). Elastic registration of geodesic vascular graphs.
Blaiotta, C., Freund, P., Cardoso, M.J., Ashburner, J. (2018). Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. NeuroImage, 166 117-134. doi:10.1016/j.neuroimage.2017.10.060
Kieselmann, J.P., Kamerling, C.P., Burgos, N., Menten, M.J., Fuller, C.D., Nill, S., ...Oelfke, U. (2018). Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region. PHYSICS IN MEDICINE AND BIOLOGY, 63 (14), doi:10.1088/1361-6560/aacb65;145007
Kieselmann, J.P., Kamerling, C.P., Burgos, N., Menten, M.J., Fuller, C.D., Nill, S., ...Oelfke, U. (2018). Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region. Physics in Medicine and Biology, 63 (14), doi:10.1088/1361-6560/aacb65
Bocchetta, M., Iglesias, J.E., Scelsi, M., Cash, D., Cardoso, M.J., Modat, M., ...Rohrer, J.D. (2018). Hippocampal subfield volumetry: differential pattern of atrophy in different forms of genetic frontotemporal dementia. Journal of Alzheimer's Disease, doi:10.3233/JAD-180195
Singleton, E.H., Pijnenburg, Y.A.L., Sudre, C.H., Kochova, E., Groot, C., Papma, J.M., ...Cardoso, M.J. (2018). IC‐P‐110: PATTERNS OF GLUCOSE HYPOMETABOLISM, SUBCORTICAL ATROPHY AND WHITE MATTER HYPERINTENSITIES IN THE BEHAVIORAL VARIANT OF ALZHEIMER'S DISEASE. Alzheimer's & Dementia, 14 (7S_Part_2), doi:10.1016/j.jalz.2018.06.2176
Canas, L.S., Yvernault, B.C., Sudre, C.H., Vita, E.D., Cardoso, M.J., Thornton, J.S., ...Modat, M. (2018). Imaging biomarkers for the diagnosis of Prion disease..
Moriconi, S., Zuluaga, M.A., Jager, H.R., Nachev, P., Ourselin, S., Cardoso, M.J. (2018). Inference of Cerebrovascular Topology with Geodesic Minimum Spanning Trees. IEEE Transactions on Medical Imaging, doi:10.1109/TMI.2018.2860239
Ranzini, M.B.M., Ebner, M., Cardoso, M.J., Fotiadou, A., Vercauteren, T., Henckel, J., ...Modat, M. (2018). Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients.
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D.I., Wang, G., ...Hu, Y. (2018). NiftyNet: a deep-learning platform for medical imaging. Computer Methods and Programs in Biomedicine, 158 113-122. doi:10.1016/j.cmpb.2018.01.025
Bisdas, S., Tisca, C., Sudre, C., Sanverdi, E., Roettger, D., Cardoso, J.M. (2018). Non-invasive in vivo prediction of tumour grade and IDH mutation status in gliomas using dynamic susceptibility contrast (DSC) perfusion-and diffusion-weighted MRI..
Lane, C.A., Sudre, C.H., Barnes, J., Nicholas, J.M., Hardy, R., Parker, T.D., ...Malone, I.B. (2018). O2‐05‐01: INFLUENCES OF BLOOD PRESSURE AND BLOOD PRESSURE TRAJECTORIES ON CEREBRAL PATHOLOGY AT AGE 70: RESULTS FROM A BRITISH BIRTH COHORT. Alzheimer's & Dementia, 14 (7S_Part_11), doi:10.1016/j.jalz.2018.06.2663
Salvadó, G., Brugulat-Serrat, A., Sudre, C.H., Grau, O., Falcon, C., Cardoso, M.J., ...Gispert, J.D. (2018). O2‐13‐03: REGIONAL DISTRIBUTION OF WHITE MATTER HYPERINTENSITIES RELATED TO ALZHEIMER'S DISEASE RISK FACTORS IN THE ALFA COHORT. Alzheimer's & Dementia, 14 (7S_Part_12), doi:10.1016/j.jalz.2018.06.2714
Woollacott, I.O.C., Bocchetta, M., Sudre, C.H., Ridha, B.H., Strand, C., Courtney, R., ...Rossor, M.N. (2018). Pathological correlates of white matter hyperintensities in a case of progranulin mutation associated frontotemporal dementia.. Neurocase, 1-9. doi:10.1080/13554794.2018.1506039
Fiford, C.M., Ridgway, G.R., Cash, D.M., Modat, M., Nicholas, J., Manning, E.N., ...Carmichael, O.T. (2018). Patterns of progressive atrophy vary with age in Alzheimer's disease patients. Neurobiology of Aging, 63 22-32. doi:10.1016/j.neurobiolaging.2017.11.002
Varsavsky, T., Eaton-Rosen, Z., Sudre, C.H., Nachev, P., Cardoso, M.J. (2018). Pimms: Permutation invariant multi-modal segmentation.
Eshaghi, A., Marinescu, R.V., Young, A.L., Firth, N.C., Prados, F., Cardoso, M.J., ...Brownlee, W.J. (2018). Progression of regional grey matter atrophy in multiple sclerosis. BRAIN, 141 1665-1677. doi:10.1093/brain/awy088
Ghazi, M.M., Nielsen, M., Pai, A., Cardoso, M.J., Modat, M., Ourselin, S., Sørensen, L. (2018). Robust training of recurrent neural networks to handle missing data for disease progression modeling.
Scott, C.J., Jiao, J., Cardoso, M.J., Kläser, K., Melbourne, A., Markiewicz, P.J., ...Ourselin, S. (2018). Short acquisition time PET/MR pharmacokinetic modelling using CNNs.
Orbes-Arteaga, M., Cardoso, M.J., Sørensen, L., Modat, M., Ourselin, S., Nielsen, M., Pai, A. (2018). Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs.
Parker, C., Clayden, J., Cardoso, M., Rodionov, R., Duncan, J., Scott, C., ...Ourselin, S. (2018). Structural and effective connectivity in focal epilepsy. NeuroImage: Clinical, doi:10.1016/j.nicl.2017.12.020
Bocchetta, M., Gordon, E., Cardoso, M.J., Modat, M., Ourselin, S., Warren, J., Rohrer, J. (2018). Thalamic atrophy in frontotemporal dementia - not just a C9orf72 problem. NeuroImage: Clinical, doi:10.1016/j.nicl.2018.02.019
Semedo, C., Cardoso, M.J., Vos, S.B., Sudre, C.H., Bocchetta, M., Ribbens, A., ...Ourselin, S. (2018). Thalamic nuclei segmentation using tractography, population-specific priors and local fibre orientation.
Eaton-Rosen, Z., Bragman, F., Bisdas, S., Ourselin, S., Cardoso, M.J. (2018). Towards safe deep learning: Accurately quantifying biomarker uncertainty in neural network predictions.
Bragman, F.J.S., Tanno, R., Eaton-Rosen, Z., Li, W., Hawkes, D.J., Ourselin, S., ...Cardoso, M.J. (2018). Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning.
Young, A., Marinescu, R., Oxtoby, N., Bocchetta, M., Yong, K., Firth, N., ...Cardoso, J. (2018). Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nature Commun, 9 4273. doi:10.1038/s41467-018-05892-0
ten Kate, M., Sudre, C.H., den Braber, A., Konijnenberg, E., Nivard, M.G., Cardoso, M.J., ...Barkhof, F. (2018). White matter hyperintensities and vascular risk factors in monozygotic twins. Neurobiology of Aging, 66 40-48. doi:10.1016/j.neurobiolaging.2018.02.002

2017

Kate, M.T., Sudre, C.H., Braber, A., Konijnenberg, E., Cardoso, M.J., Scheltens, P., ...Visser, P.J. (2017). [IC‐P‐065]: WHITE MATTER HYPERINTENSITIES AND VASCULAR RISK FACTORS IN COGNITIVELY HEALTHY ELDERLY MONOZYGOTIC TWIN PAIRS.
Lane, C.A., Malone, I.B., Sudre, C.H., Laila Ahsan, R., Manning, E., Harper, L., ...Schott, J.M. (2017). [P1–465]: PROGRESSIVE CALLOSAL ATROPHY WITH STABLE MEMORY IMPAIRMENT IN FAMILIAL BRITISH DEMENTIA.
Woollacott, I.O.C., Bocchetta, M., Sudre, C.H., Ridha, B.H., Strand, C., Courtney, R., ...Rossor, M.N. (2017). [P2–441]: PATHOLOGICAL CORRELATES OF WHITE MATTER HYPERINTENSITIES ON CADAVERIC MRI IN PROGRANULIN‐ASSOCIATED FRONTOTEMPORAL DEMENTIA.
Lane, C.A., Sudre, C.H., Barnes, J., Nicholas, J.M., Parker, T., Cash, D.M., ...Klimova, J. (2017). [P2–545]: VASCULAR AND EARLY LIFE INFLUENCES ON CEREBROVASCULAR DISEASE IN INSIGHT 46: A SUB‐STUDY OF THE MRC NATIONAL SURVEY OF HEALTH AND DEVELOPMENT (NSHD) BRITISH BIRTH COHORT. Alzheimer's & Dementia, 13 (7S_Part_17), doi:10.1016/j.jalz.2017.06.1203
Pemberton, H., Fiford, C.M., Walsh, P., Carmichael, O.T., Biessels, G.J., Sudre, C.H., ...Barnes, J. (2017). [P4–524]: WHITE MATTER HYPERINTENSITIES ARE ASSOCIATED WITH HIPPOCAMPAL ATROPHY RATES AFTER ADJUSTING FOR OTHER VASCULAR MARKERS IN PREDEMENTIA DISEASE STAGES.
Cardoso, F., Costa, A., Senkus, E., Aapro, M., Andre, F., Barrios, C.H., ...Cardoso, M.J. (2017). 3rd ESO-ESMO international consensus guidelines for Advanced Breast Cancer (ABC 3). BREAST, 31 244-259. doi:10.1016/j.breast.2016.10.001
Cardoso, F., Costa, A., Senkus, E., Aapro, M., Andre, F., Barrios, C.H., ...Cardoso, M.J. (2017). 3rd ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 3) (vol 31, pg 244, 2017). BREAST, 32 269-270. doi:10.1016/j.breast.2017.01.001
Manning, E.N., Leung, K.K., Nicholas, J., Malone, I.B., Cardoso, M.J., Schott, J., ...Barnes, J. (2017). A Comparison of Accelerated and Non-accelerated MRI Scans for Brain Volume and Boundary Shift Integral Measures of Volume Change: Evidence from the ADNI Dataset. Neuroinformatics, doi:10.1007/s12021-017-9326-0
Prados Carrasco, F., C.a.r.d.o.s.o., Yiannakas, M., Kanber, B., Kearney, H., Wheeler-Kingshott, C., Ourselin, S. (2017). A patch-based method for lesion in-painting in the spinal cord.
Sudre, C.H., Cardoso, M.J., Frost, C., Barnes, J., Barkhof, F., Fox, N., Ourselin, S. (2017). APOE ε4 status is associated with white matter hyperintensities volume accumulation rate independent of AD diagnosis. Neurobiology of Aging, 53 67-75. doi:10.1016/j.neurobiolaging.2017.01.014
Winston, G.P., Vos, S.B., Burdett, J.L., Cardoso, M.J., Ourselin, S., Duncan, J.S. (2017). Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy.. Epilepsia, doi:10.1111/epi.13843
Prados Carrasco, F., Ozgur, Y., C.a.r.d.o.s.o., Yiannakas, M., Grussu, F., Ruberte, E., ...Ourselin, S. (2017). Boundary shift integral to compute brain and cervical spinal cord longitudinal volume changes using the same 3DT1w volumetric scans in multiple sclerosis.
Premi, E., Grassi, M., van Swieten, J., Galimberti, D., Graff, C., Masellis, M., ...Laforce, R. (2017). Cognitive reserve and TMEM106B genotype modulate brain damage in presymptomatic frontotemporal dementia: a GENFI study.. Brain : a journal of neurology, doi:10.1093/brain/awx103
Holmes, H.E., Powell, N.M., Ma, D., Ismail, O., Harrison, I.F., Wells, J.A., ...Murray, T.K. (2017). Comparison of In Vivo and Ex Vivo MRI for the Detection of Structural Abnormalities in a Mouse Model of Tauopathy. FRONTIERS IN NEUROINFORMATICS, 11 doi:10.3389/fninf.2017.00020
Katritsis, D.G., Boriani, G., Cosio, F.G., Hindricks, G., Jais, P., Josephson, M.E., ...Kuck, K.-.H. (2017). European Heart Rhythm Association (EHRA) consensus document on the management of supraventricular arrhythmias, endorsed by Heart Rhythm Society (HRS), Asia-Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulacion Cardiaca y Electrofisiologia (SOLAECE). EUROPACE, 19 (3), 465-511. doi:10.1093/europace/euw301
Guerreiro, F., Burgos, N., Dunlop, A., Wong, K., Petkar, I., Nutting, C., ...Dearnaley, D. (2017). Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning. Physica Medica, doi:10.1016/j.ejmp.2017.02.017
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Cardoso, M.J. (2017). Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations..
Blaiotta, C., Freund, P., Cardoso, M.J., Ashburner, J. (2017). Generative diffeomorphic atlas construction from brain and spinal cord MRI data.
Burgos, N., Samper-González, J., Bertrand, A., Habert, M.O., Ourselin, S., Durrleman, S., ...Colliot, O. (2017). Individual analysis of molecular brain imaging data through automatic identification of abnormality patterns.
Samson, R.S., Cardoso, M.J., Muhlert, N., Sethi, V., Yaldizi, O., Ron, M.A., ...Wheeler-Kingshott, C.A.M. (2017). Investigation of outer and inner cerebellar MTR abnormalities in different MS clinical subtypes.
Burgos, N., Guerreiro, F., McClelland, J., Presles, B., Modat, M., Nill, S., ...Knopf, A. (2017). Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning. Physics in Medicine and Biology, doi:10.1088/1361-6560/aa66bf
Carass, A., Roy, S., Jog, A., Cuzzocreo, J.L., Magrath, E., Gherman, A., ...Sudre, C.H. (2017). Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. NEUROIMAGE, 148 77-102. doi:10.1016/j.neuroimage.2016.12.064
Samson, R.S., Cardoso, M.J., Muhlert, N., Sethi, V., Yaldizli, Ö., Ron, M.A., ...Wheeler-Kingshott, C.A.M. (2017). Longitudinal outer and inner cortical MTR abnormalities in different MS clinical phenotypes.
Sudre, C.H., Cardoso, M.J., Ourselin, S. (2017). Longitudinal segmentation of age-related white matter hyperintensities. MEDICAL IMAGE ANALYSIS, 38 50-64. doi:10.1016/j.media.2017.02.007
Li, W., Wang, G., Fidon, L., Ourselin, S., C.a.r.d.o.s.o., V.e.r.c.a.u.t.e.r.e.n. (2017). On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task.
Prados Carrasco, F., Nikitichev, D., Cardoso, M.J., Vercauteren, T., Ourselin, S. (2017). Patient-specific 3D Printable Anatomical Brain Models from a Web App.
Arbel, T., Cardoso, M.J., Chung, A.C.S., Jenkinson, M., Ribbens, A., Wells, W.M. (2017). Preface BAMBI 2016. .
Kinnunen, K.M., Cash, D.M., Poole, T., Frost, C., Benzinger, T.L.S., Ahsan, R.L., ...Malone, I.B. (2017). Presymptomatic atrophy in autosomal dominant Alzheimer’s disease: a serial MRI study. Alzheimer's and Dementia, doi:10.1016/j.jalz.2017.06.2268
Cordeiro, M.F., Normando, E.M., Cardoso, M.J., Miodragovic, S., Jeylani, S., Davis, B.M., ...Bloom, P.A. (2017). Real-time imaging of single neuronal cell apoptosis in patients with glaucoma.. Brain, doi:10.1093/brain/awx088
Scott, C.J., Jiao, J., Cardoso, M.J., Melbourne, A., De Vita, E., Thomas, D.L., ...Hutton, B.F. (2017). Short acquisition time PET quantification using MRI-based pharmacokinetic parameter synthesis.
Prados Carrasco, F., Ashburner, J., Blaiotta, C., Brosch, T., Carballido-Gamio, J., Cardoso, M.J., ...De Leener, B. (2017). Spinal cord grey matter segmentation challenge. Neuroimage, doi:10.1016/j.neuroimage.2017.03.010
Lane, C.A., Parker, T.D., Cash, D.M., Macpherson, K., Donnachie, E., Murray-Smith, H., ...Bras, J. (2017). Study protocol: Insight 46 - a neuroscience sub-study of the MRC National Survey of Health and Development. BMC Neurology, 17 (1), doi:10.1186/s12883-017-0846-x
Chan, D., Binks, S., Nicholas, J.M., Frost, C., Cardoso, M.J.J., Ourselin, S., ...Chataway, J. (2017). The effect of high-dose simvastatin on cognitive, neuropsychiatric and health related quality of life measures in secondary progressive multiple sclerosis: secondary and post-hoc analyses from the MS-STAT randomised placebo-controlled trial. Lancet Neurology, The, doi:10.1016/S1474-4422(17)30113-8
Harding, S.R., Bocchetta, M., Gordon, E., Cash, D.M., Cardoso, M.J., Druyeh, R., ...Rohrer, J.D. (2017). The TMEM106B risk allele is associated with lower cortical volumes in a clinically diagnosed frontotemporal dementia cohort.. Journal of neurology, neurosurgery, and psychiatry, doi:10.1136/jnnp-2017-315641
Ebner, M., Chung, K., Prados Carrasco, F., Cardoso, M.J., Chard, D.T., Vercauteren, T., Ourselin, S. (2017). Volumetric reconstruction from printed films: Enabling 30 year longitudinal analysis in MR neuroimaging.. NeuroImage, doi:10.1016/j.neuroimage.2017.09.056
Moriconi, S., Zuluaga, M.A., Jäger, H.R., Nachev, P., Ourselin, S., Cardoso, M.J. (2017). VTrails: Inferring vessels with geodesic connectivity trees.
Sudre, C.H., Bocchetta, M., Cash, D., Thomas, D.L., Woollacott, I., Dick, K.M., ...Masellis, M. (2017). White matter hyperintensities are seen only in GRN mutation carriers in the GENFI cohort. NeuroImage: Clinical, 15 171-180. doi:10.1016/j.nicl.2017.04.015
Sudre, C.H., Bocchetta, M., Cash, D.M., Thomas, D.L., Woollacott, I.O.C., Dick, K.M., ...Masellis, M. (2017). WHITE MATTER HYPERINTENSITIES IN GENETIC FRONTOTEMPORAL DEMENTIA: A GENFI STUDY.

2016

Ladefoged, C.N., Law, I., Anazodo, U., Lawrence, K.S., Izquierdo-Garcia, D., Catana, C., ...Hutton, B. (2016). A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients.. Neuroimage, doi:10.1016/j.neuroimage.2016.12.010
Prados Carrasco, F., Cardoso, M.J., Kanber, B., Ciccarelli, O., Kapoor, R., Wheeler-Kingshott, C.A.M., Ourselin, S. (2016). A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis.. NeuroImage,
Lehmann, M., Melbourne, A., Dickson, J.C., Ahmed, R.M., Modat, M., Cardoso, M.J., ...Warren, J.D. (2016). A novel use of arterial spin labelling MRI to demonstrate focal hypoperfusion in individuals with posterior cortical atrophy: A multimodal imaging study. Journal of Neurology, Neurosurgery and Psychiatry, 87 (9), 1032-1034. doi:10.1136/jnnp-2015-312782
Niedworok, C.J., Brown, A.P.Y., Jorge Cardoso, M., Osten, P., Ourselin, S., Modat, M., Margrie, T.W. (2016). AMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data. Nature Communications, 7 doi:10.1038/ncomms11879
Young, J., Modat, M., Cardoso, M.J., Ashburner, J., Ourselin, S. (2016). An Oblique Approach to Prediction of Conversion to Alzheimer's Disease with Multikernel Gaussian Processes.
Melbourne, A., Orasanu, E., Eaton-Rosen, Z., Cardoso, M.J., Beckmann, J., Smith, L., ...Ourselin, S. (2016). Analysis of brain volume in a 19 year-old extremely-preterm born cohort.
Prados Carrasco, F., Yiannakas, M.C., Cardoso, M.J., Grussu, F., De Angelis, F., Plantone, D., ...Ourselin, S. (2016). Atrophy computation in the spinal cord using the Boundary Shift Integral.
Prados Carrasco, F., Solanky, B.S., Alves Da Mota, P., Cardoso, M.J., Brownlee, W.J., Riemer, F., ...Ourselin, S. (2016). Automatic sodium maps reconstruction using PatchMatch algorithm for phantom detection.
Eaton-Rosen, Z., Melbourne, A., Jorge Cardoso, M., Marlow, N., Ourselin, S. (2016). Beyond the resolution limit: Diffusion parameter estimation in partial volume.
Gordon, E., Bocchetta, M., Cardoso, M.J., Ourselin, S., Warren, J.D., Rohrer, J.D. (2016). Clinical, genetic and pathological stratification in frontotemporal dementia: implications for clinical trial design.
Prados Carrasco, F., Y.i.a.n.n.a.k.a.s., C.a.r.d.o.s.o., G.r.u.s.s.u., De, a.n.g.e.l.i.s., P.l.a.n.t.o.n.e., ......O.u.r.s.e.l.i.n. (2016). Computing spinal cord atrophy using the Boundary Shift Integral: a more powerful outcome measure for clinical trials?.
Orasanu, E.T., Melbourne, A., C.a.r.d.o.s.o. (2016). Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching. Brain and Behavior, doi:10.1002/brb3.488
Orasanu, E., Melbourne, A., Cardoso, M.J., Lombaert, H., Kendall, G.S., Robertson, N.J., ...Ourselin, S. (2016). Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching (vol 6, pg 1, 2016). BRAIN AND BEHAVIOR, 6 (9), doi:10.1002/brb3.585
Arbel, T., Cardoso, M.J., Wells, W., Chung, A.C.S., Precup, D. (2016). Editorial on Special Issue on Probabilistic Models for Biomedical Image Analysis. COMPUTER VISION AND IMAGE UNDERSTANDING, 151 1-2. doi:10.1016/j.cviu.2016.08.011
Eaton-Rosen, Z., Cardoso, M.J., Melbourne, A., Orasanu, E., Bainbridge, A., Kendall, G.S., ...Ourselin, S. (2016). Fitting parametric models of diffusion MRI in regions of partial volume.
Prados Carrasco, F., Cardoso, M.J., Yiannakas, M.C., Hoy, L.R., Tebaldi, E., Kearney, H., ...Ourselin, S. (2016). Fully automated grey and white matter segmentation of the cervical cord in vivo.
Prados Carrasco, F., C.a.r.d.o.s.o., Y.i.a.n.n.a.k.a.s., H.o.y., Tebaldi, E., K.e.a.r.n.e.y., ......W.h.e.e.l.e.r.-.K.i.n.g.s.h.o.t.t. (2016). Fully automated grey and white matter spinal cord segmentation. Scientific Reports, doi:10.1038/srep36151
Prados, F., Cardoso, M.J., Cawley, N., Kanber, B., Ciccarelli, O., Wheeler-Kingshott, C.A.M.G., Ourselin, S. (2016). Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting.
Powell, N.M., Modat, M., Cardoso, M.J., Ma, D., Holmes, H.E., Yu, Y., ...Wiseman, F.K. (2016). Fully-Automated mu MRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome. PLOS ONE, 11 (9), doi:10.1371/journal.pone.0162974
Holmes, H.E., Colgan, N., Ismail, O., Ma, D., Powell, N.M., O'Callaghan, J.M., ...Ahmed, Z. (2016). Imaging the accumulation and suppression of tau pathology using multiparametric MRI. Neurobiology of Aging, 39 184-194. doi:10.1016/j.neurobiolaging.2015.12.001
Brownlee, W.J., Da Mota, P.A., Prados, F., Solanky, B., Riemer, F., Cardoso, M.J., ...Miller, D.H. (2016). Increased cortical and deep grey matter sodium concentration is associated with physical and cognitive disability in relapse-onset multiple sclerosis..
Burgos, N.F., Guerreiro, F., McClelland, J., Nill, S., Dearnaley, D., deSouza, N., ...Cardoso, M.J. (2016). Joint Segmentation and CT Synthesis for MRI-only Radiotherapy Treatment Planning.
Melbourne, A., Eaton-Rosen, Z., Orasanu, E., Price, D., Bainbridge, A., Cardoso, M.J., ...Ourselin, S. (2016). Longitudinal development in the preterm thalamus and posterior white matter: MRI correlations between diffusion weighted imaging and T2 relaxometry. Human Brain Mapping, 37 (7), 2479-2492. doi:10.1002/hbm.23188
Steventon, J.J., Trueman, R.C., Ma, D., Yhnell, E., Bayram-Weston, Z., Modat, M., ...Stewart, A. (2016). Longitudinal in vivo MRI in a Huntington's disease mouse model: Global atrophy in the absence of white matter microstructural damage. Scientific Reports, 6 doi:10.1038/srep32423
Lorenzi, M., Simpson, I.J., Mendelson, A.F., Vos, S.B., Cardoso, M.J., Modat, M., ...Ourselin, S. (2016). Multimodal Image Analysis in Alzheimer's Disease via Statistical Modelling of Non-local Intensity Correlations. SCIENTIFIC REPORTS, 6 doi:10.1038/srep22161
Bocchetta, M., Toussaint, N., Hutel, M., Modat, M., Cardoso, M.J., Gordon, E., ...Borroni, B. (2016). Multimodal imaging analysis of C9orf72-associated FTD in the Genetic Frontotemporal dementia Initiative (GENFI) study.
Meeter, L., Dopper, E., Jiskoot, L., Sanchez-Valle, R., Graff, C., Benussi, L., ...Galimberti, D. (2016). Neurofilament light chain: a biomarker for disease onset and survival in genetic frontotemporal dementia.
Meeter, L.H., Dopper, E.G., Jiskoot, L.C., Sanchez-Valle, R., Graff, C., Benussi, L., ...Galimberti, D. (2016). Neurofilament light chain: a biomarker for genetic frontotemporal dementia.. Annals of Clinical and Translational Neurology, 3 (8), 623-636. doi:10.1002/acn3.325
Prados Carrasco, F., Cardoso, M.J., Burgos, N., Wheeler-Kingshott, C.A.M., Ourselin, S. (2016). NiftyWeb: web based platform for image processing on the cloud.
Samson, R.S., Brownlee, W.J., Cardoso, M.J., Brown, W.J.L., Pardini, M., Prados Carrasco, F., ...Miller, D.H. (2016). Outer and inner cortical MTR abnormalities in clinically isolated syndromes.
Samson, R.S., Cardoso, M.J., Brownlee, W.J., Brown, W.J.L., Pardini, M., Ourselin, S., ...Chard, D.T. (2016). Outer and inner cortical MTR abnormalities observed in clinically isolated syndromes.
Rohrer, J., Bocchetta, M., Gordon, E., Cash, D., Dick, K., Thomas, D., ...van Swieten, J. (2016). Patterns of longitudinal neuroanatomical change in genetic FTD: results from the Genetic FTD Initiative (GENFI).
Bocchetta, M., Cardoso, M.J., Cash, D.M., Ourselin, S., Warren, J.D., Rohrer, J.D. (2016). Patterns of regional cerebellar atrophy in genetic frontotemporal dementia. NeuroImage: Clinical, 11 287-290. doi:10.1016/j.nicl.2016.02.008
Prados Carrasco, F., Solanky, B.S., Alves da Mota, P., Cardoso, M.J., Brownlee, W.J., Riemer, F., ...Wheeler-Kingshott, C.A.M. (2016). Regional variation of total sodium concentration in the healthy human brain.
Pardini, M., Sudre, C.H., Prados, F., Yaldizli, Ö., Sethi, V., Muhlert, N., ...Ourselin, S. (2016). Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis.. Journal of neurology, neurosurgery, and psychiatry, doi:10.1136/jnnp-2016-313979
Rohrer, J.D., Woollacott, I.O.C., Dick, K.M., Brotherhood, E., Gordon, E., Fellows, A., ...Ourselin, S. (2016). Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia. NEUROLOGY, 87 (13), 1329-1336. doi:10.1212/WNL.0000000000003154
Woollacott, I., Rohrer, J., Dick, K., Brotherhood, E., Gordon, E., Fellows, A., ...Ourselin, S. (2016). Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia.
Pardini, M., Sudre, C.H., Prados, F., Yaldizli, O., Sethi, V., Muhlert, N., ...Ourselin, S. (2016). The relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis.
Harding, S.R., Bocchetta, M., Gordon, E., Cash, D.M., Cardoso, M.J., Adamson, G., ...Rohrer, J.D. (2016). TMEM106B polymorphism is associated with lower cortical volumes in a clinically diagnosed FTD cohort.
Blaiotta, C., Jorge Cardoso, M., Ashburner, J. (2016). Variational inference for medical image segmentation. Computer Vision and Image Understanding, 151 14-28. doi:10.1016/j.cviu.2016.04.004
Fiford, C.M., Manning, E.N., Bartlett, J.W., Cash, D.M., Malone, I.B., Ridgway, G.R., ...Ourselin, S. (2016). White matter hyperintensities are associated with disproportionate progressive hippocampal atrophy.. Hippocampus, doi:10.1002/hipo.22690

2015

Gutman, B.A., Fletcher, P.T., Cardoso, M.J., Fleishman, G.M., Lorenzi, M., Thompson, P.M., Ourselin, S. (2015). A Riemannian Framework for Intrinsic Comparison of Closed Genus-Zero Shapes. Information processing in medical imaging : proceedings of the ... conference, 24 205-218.
Gutman, B.A., Thomas Fletcher, P., Jorge Cardoso, M., Fleishman, G.M., Lorenzi, M., Thompson, P.M., Ourselin, S. (2015). A Riemannian framework for intrinsic comparison of closed Genus-Zero shapes. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9123 205-218. doi:10.1007/978-3-319-19992-4_16
Andrews, K.A., Frost, C., Modat, M., Cardoso, M.J., Rowe, C.C., Villemagne, V., ...Schott, J.M. (2015). Acceleration of hippocampal atrophy rates in asymptomatic amyloidosis. Neurobiology of Aging, 39 99-107. doi:10.1016/j.neurobiolaging.2015.10.013
Cash, D.M., Frost, C., Iheme, L.O., Ünay, D., Kandemir, M., Fripp, J., ...Fischl, B. (2015). Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge. Neuroimage, 123 149-164. doi:10.1016/j.neuroimage.2015.07.087
Klemt, C., Modat, M., Pichat, J., Cardoso, M.J., Henckel, J., Hart, A., Ourselin, S. (2015). Automatic assessment of volume asymmetries applied to hip abductor muscles in patients with hip arthroplasty.
Burgos, N., Cardoso, M.J., Modat, M., Punwani, S., Atkinson, D., Arridge, S., ...Ourselin, S. (2015). CT synthesis in the head & neck region for PET/MR attenuation correction: an iterative multi-atlas approach.
Jiao, J., Markiewicz, P., Burgos, N., Atkinson, D., Hutton, B., Arridge, S., Ourselin, S. (2015). Detail-Preserving PET reconstruction with sparse image representation and anatomical priors.
Manning, E.N., Macdonald, K.E., Leung, K.K., Young, J., Pepple, T., Lehmann, M., ...Ourselin, S. (2015). Differential hippocampal shapes in posterior cortical atrophy patients: A comparison with control and typical AD subjects. Human Brain Mapping, n/a. doi:10.1002/hbm.22999
Mota, A., Cuplov, V., Drobnjak, I., Dickson, J., Bert, J., Burgos, N., ...Schott, J. (2015). Erratum to: Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MR(EJNMMI Physics).
Mota, A., Cuplov, V., Schott, J., Hutton, B., Thielemans, K., Drobnjak, I., ...Cardoso, J. (2015). Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MR..
Išgum, I., Benders, M.J.N.L., Avants, B., Cardoso, M.J., Counsell, S.J., Gomez, E.F., ...Makropoulos, A. (2015). Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge.. MEDICAL IMAGE ANALYSIS, 20 (1), 135-151. doi:10.1016/j.media.2014.11.001
Cardoso, M.J., Modat, M., Wolz, R., Melbourne, A., Cash, D., Rueckert, D., Ourselin, S. (2015). Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion. IEEE Transactions on Medical Imaging, 34 (9), 1976-1988. doi:10.1109/TMI.2015.2418298
Ma, D., Cardoso, M.J., Zuluaga, M.A., Modat, M., Powell, N., Wiseman, F., ...Ourselin, S. (2015). Grey matter sublayer thickness estimation in the mouse cerebellum.
Young, J., Mendelson, A., Cardoso, M.J., Modat, M., Ashburner, J., Ourselin, S. (2015). Improving MRI Brain Image Classification with Anatomical Regional Kernels.
Wells, J.A., O'Callaghan, J.M., Holmes, H.E., Powell, N.M., Johnson, R.A., Siow, B., ...Golay, X. (2015). In vivo imaging of tau pathology using multi-parametric quantitative MRI. Neuroimage, 111 369-378. doi:10.1016/j.neuroimage.2015.02.023
Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Cardoso, M.J., Modat, M., Bainbridge, A., ...Ourselin, S. (2015). Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI. NeuroImage, 111 580-589. doi:10.1016/j.neuroimage.2015.02.010
Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Cardoso, M.J., Modat, M., Bainbridge, A., ...Ourselin, S. (2015). Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI.. Neuroimage, 111 580-589. doi:10.1016/j.neuroimage.2015.02.010
Prados, F., Cardoso, M.J., Leung, K.K., Cash, D.M., Modat, M., Fox, N.C., ...Alzheimer's Disease Neuroimaging, I.n.i.t.i.a.t.i.v.e. (2015). Measuring brain atrophy with a generalized formulation of the boundary shift integral.. Neurobiol Aging, 36 (1), S81-S90. doi:10.1016/j.neurobiolaging.2014.04.035
Melbourne, A., Eaton-Rosen, Z., Owen, D., Cardoso, J., Beckmann, J., Atkinson, D., ...Ourselin, S. (2015). Measuring Cortical Neurite-Dispersion and Perfusion in Preterm-Born Adolescents Using Multi-modal MRI.
Burgos, N., Cardoso, M.J., Thielemans, K., Modat, M., Dickson, J., Schott, J., ...Ourselin, S. (2015). Multi-contrast attenuation map synthesis for PET/MR scanners: assessment on FDG and Florbetapir PET tracers. European Journal of Nuclear Medicine and Molecular Imaging, 42 (9), 1447-1458. doi:10.1007/s00259-015-3082-x
Ourselin, S., Alexander, D.C., Westin, C.F., Cardoso, M.J. (2015). Preface. .
Ourselin, S., Alexander, D.C., Westin, C.F., Cardoso, M.J. (2015). Preface. 24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28 - July 3, 2015. Proceedings. Information processing in medical imaging : proceedings of the ... conference, 24 V-VII.
Orasanu, E., Melbourne, A., Lombaert, H., Cardoso, M.J., Johnsen, S.F., Kendall, G.S., ...Ourselin, S. (2015). Prefrontal Cortical Folding of the Preterm Brain: A Longitudinal Analysis of Preterm-Born Neonates. SPATIO-TEMPORAL IMAGE ANALYSIS FOR LONGITUDINAL AND TIME-SERIES IMAGE DATA, 8682 3-12. doi:10.1007/978-3-319-14905-9_1
Rohrer, J.D., Nicholas, J.M., Cash, D.M., van Swieten, J., Dopper, E., Jiskoot, L., ...Clegg, S. (2015). Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal dementia Initiative (GENFI) study: a cross-sectional analysis.. Lancet Neurol, 14 (3), 253-262. doi:10.1016/S1474-4422(14)70324-2
Simpson, I.J., Cardoso, M.J., Modat, M., Cash, D.M., Woolrich, M.W., Andersson, J.L., ...Ourselin, S. (2015). Probabilistic non-linear registration with spatially adaptive regularisation. Medical Image Analysis, 26 (1), 203-216. doi:10.1016/j.media.2015.08.006
Petitjean, C., Zuluaga, M.A., Bai, W., Dacher, J.-.N., Grosgeorge, D., Caudron, J., ...Chen, H.-.C. (2015). Right ventricle segmentation from cardiac MRI: a collation study.. MEDICAL IMAGE ANALYSIS, 19 (1), 187-202. doi:10.1016/j.media.2014.10.004
Burgos, N.F., Cardoso, M.J., Guerreiro, F., Veiga, C., Modat, M., McClelland, J., ...Arridge, S.R. (2015). Robust CT Synthesis for Radiotherapy Planning: Application to the Head & Neck Region.
Cardoso, M.J., Modat, M., Vercauteren, T., Ourselin, S. (2015). Scale factor point spread function matching: beyond aliasing in image resampling.
Kochan, M., Daga, P., Burgos, N., White, M., Cardoso, M.J., Mancini, L., ...Yousry, T. (2015). Simulated field maps for susceptibility artefact correction in interventional MRI. International Journal of Computer Assisted Radiology and Surgery, 10 (9), 1405-1416. doi:10.1007/s11548-015-1253-7
Zuluaga Valencia, M.A., Rodionov, R., Nowell, M., Achhala, S., Zombori, G., Mendelson, A.F., ...Duncan, J.S. (2015). Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning. International Journal of Computer Assisted Radiology and Surgery,
Burgos, N.F., Cardoso, M.J., Mendelson, A.F., Schott, J.M., Atkinson, D., Arridge, S.R., ...Ourselin, S. (2015). Subject-specific Models for the Analysis of Pathological FDG PET Data.
Dingwall, N., Chalk, A., Martin, T.I., Scott, C.J., Semedo, C., Le, Q., ...Marlow, N. (2015). T2 relaxometry in the extremely-preterm brain at adolescence. MAGNETIC RESONANCE IMAGING, 34 (4), 508-514. doi:10.1016/j.mri.2015.12.020
Cardoso, M.J., Sudre, C.H., Modat, M., Ourselin, S. (2015). Template-based multimodal joint generative model of brain data.
Bocchetta, M., Gordon, E., Marshall, C.R., Slattery, C.F., Cardoso, M.J., Cash, D.M., ...Frisoni, G.B. (2015). The habenula: an under-recognised area of importance in frontotemporal dementia?. Journal of Neurology, Neurosurgery and Psychiatry, doi:10.1136/jnnp-2015-312067
Weston, P.S., Paterson, R.W., Modat, M., Burgos, N., Cardoso, M.J., Magdalinou, N., ...Bomanji, J.B. (2015). Using florbetapir positron emission tomography to explore cerebrospinal fluid cut points and gray zones in small sample sizes. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 1 (4), 440-446. doi:10.1016/j.dadm.2015.10.001
Hoang Duc, A.K., Eminowicz, G., Mendes, R., Wong, S.L., McClelland, J., Modat, M., ...Kadir, T. (2015). Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer. Medical Physics, 42 (9), 5027-5034. doi:10.1118/1.4927567

2014

Prados, F., Cardoso, M.J., MacManus, D., Wheeler-Kingshott, C.A.M., Ourselin, S. (2014). A Modality-Agnostic Patch-Based Technique for Lesion Filling in Multiple Sclerosis. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2014, PT II, 8674 781-788.
Prados, F., Cardoso, M.J., MacManus, D., Wheeler-Kingshott, C.A., Ourselin, S. (2014). A modality-agnostic patch-based technique for lesion filling in multiple sclerosis.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 (Pt 2), 781-788.
Burgos, N., Cardoso, M.J., Thielemans, K., Modat, M., Pedemonte, S., Dickson, J., ...Schott, J.M. (2014). Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies.. IEEE Trans Med Imaging, 33 (12), 2332-2341. doi:10.1109/TMI.2014.2340135
Burgos, N., Cardoso, M.J., Thielemans, K., Duncan, J.S., Atkinson, D., Arridge, S.R., ...Ourselin, S. (2014). Attenuation correction synthesis for hybrid PETMR scanners: Validation for brain study applications.
Ma, D., Cardoso, M.J., Modat, M., Powell, N., Wells, J., Holmes, H., ...Lythgoe, M.F. (2014). Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion. PLoS ONE, 9 e86576. doi:10.1371/journal.pone.0086576
(2014). Bayesian and grAphical Models for Biomedical Imaging - First International Workshop, BAMBI 2014, Cambridge, MA, USA, September 18, 2014, Revised Selected Papers.
Sudre, C.H., Cardoso, M.J., Bouvy, W., Biessels, G.J., Barnes, J., Ourselin, S. (2014). Bayesian model selection for pathological data..
Sudre, C.H., Cardoso, M.J., Bouvy, W.H., Biessels, G.J., Barnes, J., Ourselin, S. (2014). Bayesian model selection for pathological data..
Orasanu, E., Melbourne, A., Cardoso, M.J., Modat, M., Taylor, A.M., Thayyil, S., Ourselin, S. (2014). Brain volume estimation from post-mortem newborn and fetal MRI.. Neuroimage Clin, 6 438-444. doi:10.1016/j.nicl.2014.10.007
Parker, C.S., Deligianni, F., Cardoso, M.J., Daga, P., Modat, M., Dayan, M., ...Clayden, J.D. (2014). Consensus between pipelines in structural brain networks.. PLoS One, 9 (10), doi:10.1371/journal.pone.0111262
Burgos, N., Thielemans, K., Cardoso, M.J., Markiewicz, P., Jiao, J., Dickson, J., ...Hutton, B.F. (2014). Effect of scatter correction when comparing attenuation maps: Application to brain PET/MR.
Samson, R.S., Cardoso, M.J., Muhlert, N., Sethi, V., Wheeler-Kingshott, C.A., Ron, M., ...Chard, D.T. (2014). Investigation of outer cortical magnetisation transfer ratio abnormalities in multiple sclerosis clinical subgroups.. Mult Scler, 20 ((10)), 1322-1330. doi:10.1177/1352458514522537
Oxtoby, N.P., Young, A.L., Fox, N.C., Daga, P., Cash, D.M., Ourselin, S., ......o.t.h.e.r.s. (2014). Learning imaging biomarker trajectories from noisy Alzheimer’s disease data using a Bayesian multilevel model.
Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Modat, M., Cardoso, M.J., Bainbridge, A., ...Ourselin, S. (2014). Longitudinal Measurement of the Developing Thalamus in the Preterm Brain Using Multi-modal MRI. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2014, PT II, 8674 276-283.
Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Modat, M., Cardoso, M.J., Bainbridge, A., ...Ourselin, S. (2014). Longitudinal measurement of the developing thalamus in the preterm brain using multi-modal MRI.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 276-283.
Eaton-Rosen, Z., Melbourne, A., Orasanu, E., Modat, M., Cardoso, M.J., Bainbridge, A., ...Ourselin, S. (2014). Longitudinal measurement of the developing thalamus in the preterm brain using multi-modal MRI.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 (Pt 2), 276-283.
Zuluaga, M.A., Mendelson, A., Cardoso, M.J., Taylor, A.M., Ourselin, S. (2014). Multi-atlas based pathological stratification of D-TGA congenital heart disease. 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, 109-112. doi:10.1109/isbi.2014.6867821
Melbourne, A., Eaton-Rosen, Z., De Vita, E., Bainbridge, A., Cardoso, M.J., Price, D., ...Marlow, N. (2014). Multi-modal measurement of the myelin-to-axon diameter g-ratio in preterm-born neonates and adult controls.
Melbourne, A., Eaton-Rosen, Z., De Vita, E., Bainbridge, A., Cardoso, M.J., Price, D., ...Marlow, N. (2014). Multi-modal measurement of the myelin-to-axon diameter g-ratio in preterm-born neonates and adult controls.. Med Image Comput Comput Assist Interv, 17 (Pt 2), 268-275.
Zuluaga, M.A., Mendelson, A., Cardoso, M.J., Taylor, A.M., Ourselin, S. (2014). MUTI-ATLAS BASED PATHOLOGICAL STRATIFICATION OF D-TGA CONGENITAL HEART DISEASE.
Melbourne, A., Kendall, G.S., Cardoso, M.J., Gunny, R., Robertson, N.J., Marlow, N., Ourselin, S. (2014). Preterm birth affects the developmental synergy between cortical folding and cortical connectivity observed on multimodal MRI.. NEUROIMAGE, 89 23-34. doi:10.1016/j.neuroimage.2013.11.048
Zuluaga, M.A., Rodionov, R., Nowell, M., Achhala, S., Zombori, G., Cardoso, M.J., ...Ourselin, S. (2014). SEEG trajectory planning: combining stability, structure and scale in vessel extraction.
Zuluaga, M.A., Rodionov, R., Nowell, M., Achhala, S., Zombori, G., Cardoso, M.J., ...Ourselin, S. (2014). SEEG trajectory planning: combining stability, structure and scale in vessel extraction.. Med Image Comput Comput Assist Interv, 17 (Pt 2), 651-658.
Kochan, M., Daga, P., Burgos, N., White, M., Cardoso, M.J., Mancini, L., ...Yousry, T. (2014). Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI.
Kochan, M., Daga, P., Burgos, N., White, M., Cardoso, M.J., Mancini, L., ...Yousry, T. (2014). Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI. In Information Processing in Computer-Assisted Interventions. (pp. 226-235). Springer International Publishing.
Modat, M., Simpson, I.J.A., Cardoso, M.J., Cash, D.M., Toussaint, N., Fox, N.C., Ourselin, S. (2014). Simulating neurodegeneration through longitudinal population analysis of structural and diffusion weighted MRI data.. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2014, PT III, 8675 57-64.

2013

Simpson, I.J.A., Woolrich, M.W., Cardoso, M.J., Cash, D.M., Modat, M., Schnabel, J.A., Ourselin, S. (2013). A Bayesian Approach for Spatially Adaptive Regularisation in Non-rigid Registration..
Liang, Y., Gordon, E., Rohrer, J., Downey, L., de Silva, R., Jäger, H.R., ...Mahoney, C. (2013). A cognitive chameleon: lessons from a novel MAPT mutation case.. Neurocase, doi:10.1080/13554794.2013.826697
Young, J., Modat, M., Cardoso, M.J., Mendelson, A., Cash, D., Ourselin, S. (2013). Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. NeuroImage: clinical, 2 735-745.
Cardoso, M.J., Melbourne, A., Kendall, G.S., Modat, M., Robertson, N.J., Marlow, N., Ourselin, S. (2013). AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI.. NEUROIMAGE, 65 97-108. doi:10.1016/j.neuroimage.2012.08.009
Keihaninejad, S., Zhang, H., Ryan, N.S., Malone, I.B., Modat, M., Cardoso, J., ...Ourselin, S. (2013). An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer's disease. NeuroImage, doi:10.1016/j.neuroimage.2013.01.044
Andrews, K.A., Modat, M., Macdonald, K.E., Yeatman, T., Cardoso, M.J., Leung, K.K., ...Fox, N.C. (2013). Atrophy Rates in Asymptomatic Amyloidosis: Implications for Alzheimer Prevention Trials. PLoS ONE, 8 (3), e58816-. doi:10.1371/journal.pone.0058816
Burgos, N., Cardoso, J.M., Modat, M., Pedemonte, S., Dickson, J., Barnes, A., ...Hutton, B.F. (2013). Attenuation Correction Synthesis for Hybrid PET-MR Scanners.
Winston, G.P., Cardoso, M.J., Williams, E.J., Burdett, J.L., Bartlett, P.A., Espak, M., ...Ourselin, S. (2013). Automated hippocampal segmentation in patients with epilepsy: available free online.. Epilepsia, 54 (12), 2166-2173. doi:10.1111/epi.12408
Irani, S.R., Stagg, C.J., Schott, J.M., Rosenthal, C.R., Schneider, S.A., Pettingill, P., ...Voets, N.L. (2013). Faciobrachial dystonic seizures: the influence of immunotherapy on seizure control and prevention of cognitive impairment in a broadening phenotype.. BRAIN, 136 3151-3162. doi:10.1093/brain/awt212
Melbourne, A., Eaton-Rosen, Z., Bainbridge, A., Kendall, G.S., Cardoso, M.J., Robertson, N.J., ...Ourselin, S. (2013). Measurement of Myelin in the Preterm Brain: Multi-compartment Diffusion Imaging and Multi-component T2 Relaxometry..
Winston, G.P., Cardoso, M.J., Williams, E.J., Burdett, J.L., Bartlett, P.A., Espak, M., ...Ourselin, S. (2013). Online automated hippocampal segmentation in patients with epilepsy.
Petersen, J., Modat, M., Cardoso, M.J., Dirksen, A., Ourselin, S., Bruijne, M.D. (2013). Quantitative Airway Analysis in Longitudinal Studies Using Groupwise Registration and 4D Optimal Surfaces..
Petersen, J., Modat, M., Cardoso, M.J., Dirksen, A., Ourselin, S., de Bruijne, M. (2013). Quantitative airway analysis in longitudinal studies using groupwise registration and 4D optimal surfaces.. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 16 (Pt 2), 287-294.
Jorge Cardoso, M., Leung, K., Modat, M., Keihaninejad, S., Cash, D., Barnes, J., ...Alzheimer’s Disease Neuroimaging, I.n.i.t.i.a.t.i.v.e. (2013). STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation.. Medical Image Analysis, 17 (6), 671-684. doi:10.1016/j.media.2013.02.006
Andrews, K., Abdelsamed, H., Yi, A.-.K., Miller, M.A., Fitzpatrick, E.A. (2013). TLR2 Regulates Neutrophil Recruitment and Cytokine Production with Minor Contributions from TLR9 during Hypersensitivity Pneumonitis. PLOS ONE, 8 (8), doi:10.1371/journal.pone.0073143
Hoang Duc, A.K., Modat, M., Leung, K.K., Cardoso, M.J., Barnes, J., Kadir, T., Ourselin, S. (2013). Using Manifold Learning for Atlas Selection in Multi-Atlas Segmentation. PLoS One, 8 (8), doi:10.1371/journal.pone.0070059

2012

Vonach, M., Marson, B., Yun, M., Cardoso, J., Modat, M., Ourselin, S., Holder, D. (2012). A method for rapid production of subject specific finite element meshes for electrical impedance tomography of the human head.. Physiological measurement, 33 (5), 801-.
Modat, M., Leung, K.K., Cardoso, M.J., Fox, N.C. (2012). Atrophy Measurement Based on Segmentation Propagation and the Boundary Shift Integral Technique.
Zuluaga, M.A., Cardoso, M.J., Ourselin, S. (2012). Automatic right ventricle segmentation using multi-label fusion in cardiac MRI.
Young, J., Modat, M., Cardoso, M.J., Ashburner, J., Ourselin, S. (2012). Classification of Alzheimer's disease patients and controls with Gaussian processes..
Cash, D.M., Melbourne, A., Modat, M., Machado, M., Clarkson, M., Fox, N., Ourselin, S. (2012). Cortical Folding Analysis on Patients with Alzheimer’s Disease and Mild Cognitive Impairment.
Han, L., Hipwell, J.H., Tanner, C., Taylor, Z., Mertzanidou, T., Cardoso, J., ...Hawkes, D.J. (2012). Development of patient-specific biomechanical models for predicting large breast deformation.. PHYSICS IN MEDICINE AND BIOLOGY, 57 (2), 455-472. doi:10.1088/0031-9155/57/2/455
Cardoso, J.L., Hernández-Saldaña, H. (2012). Engineering a spin-fet: spin-orbit phenomena and spin transport induced by a gate electric field.
Cardoso, M.J., Wolz, R., Modat, M., Fox, N.C., Rueckert, D., Ourselin, S. (2012). Geodesic information flows.
Cardoso, M.J., Wolz, R., Modat, M., Fox, N.C., Rueckert, D., Ourselin, S. (2012). Geodesic information flows..
Cardoso, M.J., Winston, G., Modat, M., Keihaninejad, S., Duncan, J., Ourselin, S. (2012). Geodesic shape-based averaging..
Modat, M., Cardoso, M.J., Daga, P., Cash, D.M., Fox, N.C., Ourselin, S. (2012). Inverse-Consistent Symmetric Free Form Deformation..
Mertzanidou, T., Hipwell, J., Cardoso, M.J., Zhang, X., Tanner, C., Ourselin, S., ...Hawkes, D. (2012). MRI to X-ray mammography registration using a volume-preserving affine transformation.. MEDICAL IMAGE ANALYSIS, 16 (5), 966-975. doi:10.1016/j.media.2012.03.001
Samson, R., Cardoso, M., Muhlert, N., Sethi, V., Wheeler-Kingshott, C., Ourselin, S., ...Chard, D. (2012). MTR analysis of inner and outer cortical bands in multiple sclerosis.
Ma, D., Cardoso, M., Modat, M., Powell, N., Holmes, H., Lythgoe, M., Ourselin, S. (2012). Multi Atlas Segmentation applied to in vivo mouse brain MRI.
Cardoso, M.J., Modat, M., Ourselin, S., Keihaninejad, S., Cash, D. (2012). Multi-STEPS: Multi-label similarity and truth estimation for propagated segmentations. Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, 153-158. doi:10.1109/MMBIA.2012.6164748
Modat, M., Daga, P., Cardoso, M.J., Ourselin, S., Ridgway, G.R., Ashburner, J. (2012). Parametric non-rigid registration using a stationary velocity field.
Melbourne, A., Kendall, G.S., Cardoso, M.J., Gunney, R., Robertson, N.J., Marlow, N., Ourselin, S. (2012). Radial structure in the preterm cortex; persistence of the preterm phenotype at term equivalent age?.
Pedemonte, S., Cardoso, M.J., Arridge, S.R., Hutton, B.F., Ourselin, S. (2012). Steady-State Model of the Radio-Pharmaceutical Uptake for MR-PET..

2011

Clarkson, M.J., Cardoso, M.J., Ridgway, G.R., Modat, M., Leung, K.K., Rohrer, J.D., ...Ourselin, S. (2011). A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage, 57 (3), 856-865. doi:10.1016/j.neuroimage.2011.05.053
Daga, P., Winston, G., Modat, M., White, M., Mancini, L., Cardoso, M., ...Ourselin, S. (2011). Accurate localisation of optic radiation during neurosurgery in an interventional MRI Suite. IEEE Trans Med Imaging, doi:10.1109/TMI.2011.2179668
Cardoso, M.J., Melbourne, A., Kendall, G.S., Hagmann, C., Bainbridge, A., Marlow, N., ...Ourselin, S. (2011). ADAPTIVE NEONATAL BRAIN SEGMENTATION: APPLICATION TO VENTRICULOMEGALY AND EXCESS EXTRA-AXIAL CEREBRAL-SPINAL FLUID. PEDIATRIC RESEARCH, 70 30. doi:10.1038/pr.2011.255
Cardoso, M.J., Melbourne, A., Kendall, G.S., Modat, M., Hagmann, C.F., Robertson, N.J., ...Ourselin, S. (2011). Adaptive neonate brain segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6893 LNCS (PART 3), 378-386. doi:10.1007/978-3-642-23626-6_47
Cardoso, M.J., Melbourne, A., Kendall, G.S., Modat, M., Hagmann, C.F., Robertson, N.J., ...Ourselin, S. (2011). Adaptive neonate brain segmentation..
Melbourne, A., Kendall, G.S., Cardoso, M.J., San, P., Hagman, C., Bainbridge, A., ...Robertson, N.J. (2011). AUTOMATED ANALYSIS OF THE PRETERM NEONATAL CORTEX AT TERM EQUIVALENT AGE AND CORRELATION WITH COGNITIVE OUTCOME AT 1 YEAR CORRECTED AGE. PEDIATRIC RESEARCH, 70 193. doi:10.1038/pr.2011.418
Clarkson, M.J., Cardoso, M., Modat, M., Ridgway, G.R., Leung, K.K., Rohrer, J., ...Ourselin, S. (2011). Cross-sectional analysis using voxel or surface based cortical thickness methods: a comparison study.
Daga, P., Winston, G., Modat, M., Cardoso, M.J., White, M., Mancini, L., ...Yousry, T. (2011). Improved neuronavigation through integration of intraoperative anatomical and diffusion images in an interventional MRI suite.
Daga, P., Winston, G., Modat, M., Cardoso, M.J., Stretton, J., Symms, M., ...Ourselin, S. (2011). Integrating structural and diffusion MR information for optic radiation localisation in focal epilepsy patients.
Cardoso, M.J., Clarkson, M.J., Ridgway, G.R., Modat, M., Fox, N.C., Ourselin, S., Alzheimer's Disease Neuroimaging, I.n.i.t.i.a.t.i.v.e. (2011). LoAd: a locally adaptive cortical segmentation algorithm.. NeuroImage, 56 (3), 1386-1397. doi:10.1016/j.neuroimage.2011.02.013
Modat, M., Ridgway, G.R., Daga, P., Cardoso, M.J., Hawkes, D.J., Ashburner, J., Ourselin, S. (2011). Log-Euclidean free-form deformation.
Cardoso, M.J., Clarkson, M.J., Modat, M., Ourselin, S. (2011). Longitudinal cortical thickness estimation using Khalimsky's cubic complex..
Cardoso, M.J., Clarkson, M.J., Modat, M., Ourselin, S. (2011). On the extraction of topologically correct thickness measurements using Khalimsky's cubic complex.
Roth, H.R., McClelland, J.R., Boone, D.J., Modat, M., Cardoso, M.J., Hampshire, T.E., ...Slabaugh, G.G. (2011). Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography. Medical Physics, 38 (6), 3077-3089. doi:10.1118/1.3577603
Cardoso, M.J., Clarkson, M.J., Modat, M., Talbot, H., Couprie, M., Ourselin, S. (2011). Topologically correct cortical segmentation using Khalimsky's cubic complex framework.

2010

Pedemonte, S., Cardoso, M.J., Bousse, A., Panagiotou, C., Kazantsev, D., Arridge, S., ...Ourselin, S. (2010). Class Conditional Entropic Prior for MRI Enhanced SPECT Reconstruction.
Cardoso, M.J., Clarkson, M.J., Modat, M., Ridgway, G.R., Ourselin, S. (2010). Locally weighted Markov random fields for cortical segmentation.
Lima, C.S., Tavares, A., Correia, J.H., Cardoso, M.J., Barbosa, D. (2010). Non-Stationary Biosignal Modelling. In New Developments in Biomedical Engineering. (pp. 37-72). Croatia: IN-TECH.
Mertzanidou, T., Hipwell, J.H., Cardoso, M.J., Tanner, C., Ourselin, S., Hawkes, D.J. (2010). X-ray Mammography - MRI Registration Using a Volume-Preserving Affine Transformation and an EM-MRF for Breast Tissue Classification..

2009

Cardoso, M.J., Clarkson, M.J., Ridgway, G.R., Modat, M., Fox, N.C., Ourselin, S. (2009). Improved Maximum a Posteriori Cortical Segmentation by Iterative Relaxation of Priors.
Cardoso, M.J., Clarkson, M.J., Ridgway, G.R., Modat, M., Fox, N.C., Ourselin, S. (2009). Improved maximum a posteriori cortical segmentation by iterative relaxation of priors..

2007

Lima, C.S., Cardoso, M.J. (2007). Cardiac arrhythmia detection by parameters sharing and MMIE training of Hidden Markov Models.. 2007 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-16, 3836-3839. doi:10.1109/IEMBS.2007.4353169
Lima, C.S., Cardoso, M.J. (2007). Phonocardiogram segmentation by using an hybrid RBF-HMM model.
Lima, C.S., Cardoso, M.J. (2007). Phonocardiogram segmentation by using hidden markov models.

1900

Machado Cardoso, M.J. (1900). Automated Morphometric Characterization of the Cerebral Cortex for the Developing and Ageing Brain. (Doctoral Dissertation). UCL (University College London).
Sudre, C.H. (1900). Automated segmentation and characterisation of white matter hyper intensities. (Doctoral Dissertation). UCL.
Sudre, C.H., Cardoso, M.J., Bouvy, W.H., Biessels, G.J., Barnes, J., Ourselin, S. (1900). Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation.
Sudre, C.H., Cardoso, M.J., Ourselin, S. (1900). Bilayered Anatomically-constrained Split-and-Merge Expectation Maximisation Algorithm (BiASM) for Brain Segmentation.
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., ...Rozycki, M. (1900). Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. arXiv.
Burgos, N.F. (1900). Image synthesis for the attenuation correction and analysis of PET/MR data. (Doctoral Dissertation). University College London.
Lucena, O., Borges, P., Cardoso, J., Ashkan, K., Sparks, R., Ourselin, S. (1900). Informative and Reliable Tract Segmentation for Preoperative Planning. Frontiers in Radiology, 2 doi:10.3389/fradi.2022.866974
Daga, P. (1900). Integrating structural and diffusion MR information for optic radiation localisation in focal epilepsy patients.
Graham, M.S., May, A., Varsavsky, T., Sudre, C.H., Murray, B., Kläser, K., ...Modat, M. (1900). Knowledge barriers in a national symptomatic-COVID-19 testing programme. PLOS Global Public Health, 2 (1), e0000028. doi:10.1371/journal.pgph.0000028
Ianus, A., Drobnjak, I., Alexander, D.C. (1900). Model-based estimation of microscopic anisotropy in macroscopically isotropic substrates using diffusion MRI. In Ourselin, S., Alexander, D., Westin, C.-.F., Cardoso, J. (Eds.), .
Sekine, T., Burgos, N.F., Warnock, G., Huellner, M., Buck, A., Voert, E.G.W., ...Veit-Haibach, P. (1900). Multi atlas-based attenuation correction for brain FDG-PET imaging using a TOF-PET/MR scanner—comparison with clinical single atlas- and CT-based attenuation correction. Journal of Nuclear Medicine, doi:10.2967/jnumed.115.169045
Zuluaga, M.A., Cardoso, M.J., Modat, M., Ourselin, S. (1900). Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion.