Preprints

• Augmentation based unsupervised domain adaptation
  M Orbes-Arteaga, T Varsavsky, L Sørensen, M Nielsen, A Pai, S Ourselin, M Modat, MJ Cardoso
  arXiv:2202.11486, 2022
  [Paper pre-print (PDF)] [Link (arXiv)]

• The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
  RV Marinescu, NP Oxtoby, AL Young, EE Bron, AW Toga, MW Weiner, F Barkhof, NC Fox, A Eshaghi, T Toni, M Salaterski, V Lunina, M Ansart, S Durrleman, P Lu, S Iddi, D Li, WK Thompson, MC Donohue, A Nahon, Y Levy, D Halbersberg, M Cohen, H Liao, T Li, K Yu, H Zhu, JG Tamez-Pena, A Ismail, T Wood, HC Bravo, M Nguyen, N Sun, J Feng, BTT Yeo, G Chen, K Qi, S Chen, D Qiu, I Buciuman, A Kelner, R Pop, D Rimocea, M Mehdipour Ghazi, M Nielsen, S Ourselin, L Sørensen, V Venkatraghavan, K Liu, C Rabe, P Manser, SM Hill, J Howlett, Z Huang, S Kiddle, S Mukherjee, A Rouanet, B Taschler, BDM Tom, SR White, N Faux, S Sedai, J de Velasco Oriol, EEV Clemente, K Estrada, L Aksman, A Altmann, CM Stonnington, Y Wang, J Wu, V Devadas, C Fourrier, LL Raket, A Sotiras, G Erus, J Doshi, C Davatzikos, J Vogel, A Doyle, A Tam, A Diaz-Papkovich, E Jammeh, I Koval, P Moore, TJ Lyons, J Gallacher, J Tohka, R Ciszek, B Jedynak, K Pandya, M Bilgel, W Engels, J Cole, P Golland, S Klein, DC Alexander
  arXiv:2002.03419, 2020
  [Paper pre-print (PDF)] [Link (arXiv)]

PhD theses

• Robust Modeling and Prediction of Disease Progression Using Machine Learning
  M Mehdipour Ghazi.
  Doctoral thesis (Ph.D), UCL (University College London)
  [Thesis (PDF)] [Link (UCL Discovery)]

Peer-reviewed journal papers

• Robust parametric modeling of Alzheimer’s disease progression
  M Mehdipour Ghazi, M Nielsen, A Pai, M Modat, MJ Cardoso, S Ourselin, L Sørensen.
  NeuroImage, Volume 225, 15 January 2021, 117460, 2021
  [Paper (PDF)] [Link (ScienceDirect)] [Code (GitHub)]
• Training recurrent neural networks robust to incomplete data: application to Alzheimer’s disease progression modeling
  M Mehdipour Ghazi, M Nielsen, A Pai, MJ Cardoso, M Modat, S Ourselin, L Sørensen.
  Medical Image Analysis, 59:39-46, 2019
  [Paper pre-print (PDF)] [Link (ScienceDirect)]

Peer-reviewed conference and workshop papers

• Lesion-wise evaluation for effective performance monitoring of small object segmentation
  I Groothuis, C. Sudre, S. Ingala, J. Barnes, J.D.G. Lopez, A. Pai, L. Sørensen, M. Nielsen, S. Ourselin, J. Cardoso, F. Barkhof, M. Modat
  SPIE Medical Imaging 2021, 2021
  [Link (SPIE Digital Library]
  
• On The Initialization of Long Short-Term Memory Networks
  M Mehdipour Ghazi, M Nielsen, A Pai, M Modat, MJ Cardoso, S Ourselin, L Sørensen.
  International Conference on Neural Information Processing (ICONIP 2019), 2019
  [Paper pre-print (PDF)] [Link (SpringerLink)]
  
• Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
  M Orbes-Arteaga, T Varsavsky, CH Sudre, Z Eaton-Rosen, LJ Haddow, L Sørensen, M Nielsen, A Pai, S Ourselin, M Modat, P Nachev, MJ Cardoso.
  Domain Adaptation and Representation Transfer (DART 2019), 2019
  [Paper pre-print (PDF)] [Link (SpringerLing)]
• Knowledge Distillation for Semi-Supervised Domain Adaptation
  M Orbes-Arteaga, MJ Cardoso, L Sørensen, C Igel, S Ourselin, M Modat, M Nielsen, A Pai.
  Machine Learning in Clinical Neuroimaging (MLCN 2019), 2019
  [Paper pre-print (PDF)] [Link (SpringerLink)]
• PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
  M Orbes-Arteaga, L Sørensen, MJ Cardoso, M Modat, S Ourselin, S Sommer, M Nielsen, C Igel, A Pai.
  SPIE Medical Imaging 2019, 2019
  [Extended abstract (PDF)] [Link (SPIE Digital Library)]
  
• Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
  M Orbes-Arteaga, L Sørensen, M Modat, MJ Cardoso, S Ourselin, M Nielsen, A Pai.
  International conference on Medical Imaging with Deep Learning (MIDL 2018), 2018
  [Paper (PDF)] [Link (OpenReview)]
• Robust training of recurrent neural networks to handle missing data for disease progression modeling
  M Mehdipour Ghazi, M Nielsen, A Pai, MJ Cardoso, M Modat, S Ourselin, L Sørensen.
  International conference on Medical Imaging with Deep Learning (MIDL 2018), 2018
  [Paper (PDF)] [Presentation (YouTube)] [Link (OpenReview)]

Abstracts

• Comparison of SWI and T2S for learning based microbleed segmentation
  I Groothuis, S. Ingala, C. Sudre, L. Lorenzini, A. Pai, L. Sørensen, J. Cardoso, M. Nielsen, S. Ourselin, F. Barkhof, M. Modat
  IEEE International Symposium on Biomedical Imaging (ISBI 2021), accepted
• Disease Progression Modeling-Based Prediction of Cognitive Decline
  M Mehdipour Ghazi, et. al
  The Alzheimer’s Association International Conference (AAIC 2020), 2020
• MRI Biomarkers Improve Disease Progression Modeling-based Prediction of Cognitive Decline
  M Mehdipour Ghazi, M. Nielsen, A. Pai, M. Modat, M.J. Cardoso, S. Ourselin, and L. Sørensen.
  Radiological Society of North America (RSNA 2019), 2019
  [Abstract (HTML)]

Posters

• Automatic segmentation of microbleeds in 3D MRI
  I Groothuis, C Sudre, A Pai, L Sørensen, M Nielsen, F Barkhof, S Ourselin, MJ Cardoso, M Modat.
  Medical Imaging Summer School (MISS 2018), 2018
• Parametric disease progression modeling: an empirical comparison of sigmoidal basis functions
  M Mehdipour Ghazi, M Nielsen, A Pai, MJ Cardoso, M Modat, S Ourselin, L Sørensen.
  Second International Workshop on Modelling the Progression Of Neurological Disease (POND 2018), 2018

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Relevant publications pre-project

Disease progression modeling

• A simulation system for biomarker evolution in neurodegenerative disease
  AL Young, NP Oxtoby, S Ourselin, JM Schott, DC Alexander.
  Medical image analysis 26(1):47-56, 2015
• Estimating anatomical trajectories with Bayesian mixed-effects modeling
  G Ziegler, WD Penny, GR Ridgway, S Ourselin, KJ Friston.
  NeuroImage 121: 51-68, 2015
• A data-driven model of biomarker changes in sporadic Alzheimer’s disease
  AL Young, NP Oxtoby, P Daga, DM Cash, NC Fox, S Ourselin, JM Schott, DC Alexander.
  Brain 137(9): 2564-2577, 2014
• An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease
  HM Fonteijn, M Modat, MJ Clarkson, J Barnes, M Lehmann, NZ Hobbs, RI Scahill, SJ Tabrizi, S Ourselin, NC Fox, DC Alexander.
  NeuroImage 60(3): 1880-1889, 2012

Imaging biomarkers of dementia

• Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry
  L Sørensen, C Igel, A Pai, I Balas, C Anker, M Lillholm, M Nielsen.
  NeuroImage: Clinical 13: 470-482, 2017
• Early detection of Alzheimer’s disease using MRI hippocampal texture
  L Sørensen, C Igel, NL Hansen, M Osler, M Lauritzen, E Rostrup, M Nielsen.
  Human Brain Mapping 37(3): 1148-1161, 2016
• Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation
  CH Sudre, MJ Cardoso, WH Bouvy, GJ Biessels, J Barnes, S Ourselin.
  IEEE transactions on medical imaging 34(10): 2079-2102, 2015
• Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI
  L Lillemark, L Sørensen, A Pai, EB Dam, M Nielsen.
  BMC Medical Imaging 14(1):21, 2014
• Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment
  J Young, M Modat, MJ Cardoso, A Mendelson, D Cash, S Ourselin.
  NeuroImage: Clinical 2: 735–745, 2013