Lauge Sørensen

Email:	lauges (at) di (dot) ku (dot) dk
See also:	Google Scholar page
ORCID:	0000-0002-1181-7150

Lauge Sørensen received his Master and Ph.D. degree from the Department of Computer Science at the University of Copenhagen (DIKU) in 2007 and 2010, respectively. Part of his Ph.D. program was carried out at the Pattern Recognition Laboratory at Delft University of Technology in the Netherlands. From 2010-2012 he was a Postdoc at DIKU, from 2012-2014 he was industrial Postdoc at DIKU and Biomediq A/S, from 2014-2019 he was Assistant Professor at DIKU, and from 2019-2021 he was part time Associate Professor at DIKU and was part-time Researcher at Biomediq A/S and Cerebriu A/S. Since 2021, he has been Sr. Researcher at Biomediq A/S and Cerebriu A/S.

He is working in the domain of medical image analysis with focus on imaging biomarkers of Alzheimer's disease and chronic obstructive pulmonary disease (COPD). This includes topics such as image processing and pattern recognition.

Resources

Data

• Computed Tomography Emphysema Database
• COPD Machine Learning Datasets

Code

• Continuous Autoregressive Recurrent Neural Network (CARNN) toolbox
• Robust Parametric Disease Progression Modeling (RPDPM) toolbox

Publications

The publications are categorized broadly and simplistically into the four topics methodology (), microscopy (), neurology (), and pulmonology (), and they are numbered consecutively using the following publication types: J=journal paper, C=conference paper, W=workshop paper, A=abstract, P=patent.

Pre-prints

• Augmentation based unsupervised domain adaptation
  M. Orbes-Arteaga, T. Varsavsky, L. Sørensen, M. Nielsen, A. Pai, S. Ourselin, M. Modat, M. J. Cardoso, arXiv:2202.11486, 2022
  [PDF (pre-print)][Link (arXiv)]
• The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
  R.V. Marinescu, N.P. Oxtoby, A.L. Young, E.E. Bron, A.W. Toga, M.W. Weiner, F. Barkhof, N.C. Fox, A. Eshaghi, T. Toni, M. Salaterski, V. Lunina, M. Ansart, S. Durrleman, P. Lu, S. Iddi, D. Li, W.K. Thompson, M.C. Donohue, A. Nahon, Y. Levy, D. Halbersberg, M. Cohen, H. Liao, T. Li, K. Yu, H. Zhu, J.G. Tamez-Pena, A. Ismail, T. Wood, H.C. Bravo, M. Nguyen, N. Sun, J. Feng, B.T.T. 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, S.M. Hill, J. Howlett, Z. Huang, S. Kiddle, S. Mukherjee, A. Rouanet, B. Taschler, B.D.M. Tom, S.R. White, N. Faux, S. Sedai, J. de Velasco Oriol, E.E.V. Clemente, K. Estrada, L. Aksman, A. Altmann, C.M. Stonnington, Y. Wang, J. Wu, V. Devadas, C. Fourrier, L.L. Raket, A. Sotiras, G. Erus, J. Doshi, C. Davatzikos, J. Vogel, A. Doyle, A. Tam, A. Diaz-Papkovich, E. Jammeh, I. Koval, P. Moore, T.J. Lyons, J. Gallacher, J. Tohka, R. Ciszek, B. Jedynak, K. Pandya, M. Bilgel, W. Engels, J. Cole, P. Golland, S. Klein, D.C. Alexander, arXiv:2002.03419, 2020
  [PDF (pre-print)][Link (arXiv)]

Journal papers

• [J17] CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
  M.M. Ghazi, S. Ourselin, L. Sørensen, M. Nielsen, IEEE Transactions on Neural Networks and Learning Systems, early access, 2022
  [PDF (pre-print)] [Link] [Code] [PubMed]
• [J16] Robust parametric modeling of Alzheimer's disease progression
  M.M. Ghazi, M. Nielsen, A. Pai, M. Modat, M.J. Cardoso, S. Ourselin, L. Sørensen, NeuroImage 225: 117460, 2021
  [PDF] [Link] [Code] [PubMed]
• [J15] Inflammatory Pathway Analytes Predicting Rapid Cognitive Decline in MCI Stage of Alzheimer's Disease
  J. Pillai, J. Bena, G. Bebek, L. Bekris, A. Bonner-Jackson, L. Kou, A. Pai, L. Sørensen, M. Nielsen, M. Chance, S. Rao, B. Lamb, J. Leverenz, Annals of Clinical and Translational Neurology 7(7): 1225-1239, 2020.
  [PDF] [Link] [PubMed]
• [J14] Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial
  L. Sørensen, M. Nielsen, J. Petersen, J.H. Pedersen, A. Dirksen, and M. de Bruijne, American Journal of Roentgenology 214(6): 1269-1279, 2020.
  [PDF] [Link] [PubMed]
• [J13] The Value of Hippocampal Volume, Shape and Texture for 11-year Prediction of Dementia: a Population-Based Study
  H.C. Achterberg, L. Sørensen, F.J. Wolters, W.J. Niessen, M.W. Vernooij, M.A. Ikram, M. Nielsen, and M. de Bruijne, Neurobiology of Aging 81: 58-66, 2019.
  [PDF] [Link] [PubMed]
• [J12] Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
  M.M. Ghazi, M. Nielsen, A. Pai, M.J. Cardoso, M. Modat, S. Ourselin, and L. Sørensen, Medical Image Analysis 53: 39-46, 2019.
  [PDF (preprint)] [Link] [PubMed]
• [J11] Ensemble support vector machine classification of dementia using structural MRI and Mini-Mental State Examination
  L. Sørensen and M. Nielsen, Journal of Neuroscience Methods 302: 66-74, 2018.
  [PDF (preprint)] [Link] [PubMed] [3rd place in challenge (final test set ranking)]
• [J10] Subclinical depressive symptoms during late midlife and structural brain alterations: A longitudinal study of Danish men born in 1953
  M. Osler, L. Sørensen, M. Rozing, O. Calvo, M. Nielsen, and E. Rostrup, Human Brain Mapping 39(4): 1789-1795, 2018.
  [PDF] [Link] [PubMed]
• [J9] Transfer learning for multi-center classification of chronic obstructive pulmonary disease
  V. Cheplygina, I.P. Pena, J.H. Pedersen, D.A. Lynch, L. Sørensen, and M. de Bruijne, IEEE Journal of Biomedical and Health Informatics 22(5): 1486-1496, 2018.
  [PDF (preprint)] [Data (features)] [Link] [PubMed]
• [J8] 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, and M. Nielsen, NeuroImage: Clinical 13: 470-482, 2017.
  [PDF] [Link] [PubMed] [1st place in challenge]
• [J7] Deformation-based atrophy computation by surface propagation and its application to Alzheimer's disease
  A. Pai, J. Sporring, S. Darkner, E. Dam, M. Lillholm, D. Jørgensen, J. Oh, G. Chen, J. Suhy, L. Sørensen, and M. Nielsen, Journal of Medical Imaging 3(1), 014005, 2016.
  [PDF] [Link] [PubMed]
• [J6] Kernel bundle diffeomorphic image registration using stationary velocity fields and Wendland basis functions
  A. Pai, S. Sommer, L. Sørensen, S. Darkner, J. Sporring, and M. Nielsen, IEEE Transactions on Medical Imaging 35(6): 1369-1380, 2016.
  [Link] [PubMed]
• [J5] Early detection of Alzheimer's disease using MRI hippocampal texture
  L. Sørensen, C. Igel, N.L. Hansen, M. Osler, M. Lauritzen, E. Rostrup, and M. Nielsen, Human Brain Mapping 37(3): 1148-1161, 2016.
  [PDF] [Link] [PubMed]
• [J4] Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge
  E.E. Bron, M. Smits, W.M. van der Flier, H. Vrenken, F. Barkhof, P. Scheltens, J.M. Papma, R.M. Steketee, C. M. Orellana, R. Meijboom, M. Pinto, J.R. Meireles, C. Garrett, A.J. Bastos-Leite, A. Abdulkadir, O. Ronneberger, N. Amoroso, R. Bellotti, D. Cárdenas-Peña, A.M. Álvarez-Meza, C.V. Dolph, K.M. Iftekharuddin, S.F. Eskildsen, P. Coupé, V.S. Fonov, K. Franke, C. Gaser, C. Ledig, R. Guerrero, T. Tong, K.R. Gray, E. Moradi, J. Tohka, A. Routier, S. Durrleman, A. Sarica, G. Di Fatta, F. Sensi, A. Chincarini, G.M. Smith, Z.V. Stoyanov, L. Sørensen, M. Nielsen, S. Tangaro, P. Inglese, C. Wachinger, M. Reuter, J.C. van Swieten, W.J. Niessen, and S. Klein, NeuroImage 111: 562-579, 2015.
  [PDF (preprint)] [Link] [PubMed] [1st place in challenge]
• [J3] Brain region's relative proximity as marker for Alzheimer's disease based on structural MRI
  L. Lillemark, L. Sørensen, A. Pai, E. Dam, and M. Nielsen, BMC Medical Imaging 14(1):21, 2014.
  [PDF] [Link] [PubMed]
• [J2] Texture-Based Analysis of COPD: a Data-Driven Approach
  L. Sørensen, M. Nielsen, P. Lo, H. Ashraf, J.H. Pedersen, and M. de Bruijne, IEEE Transactions on Medical Imaging 31(1): 70-78, 2012.
  [PDF (preprint)] [Link] [PubMed]
• [J1] Quantitative analysis of pulmonary emphysema using local binary patterns
  L. Sørensen, S.B. Shaker, and M. de Bruijne, IEEE Transactions on Medical Imaging 29(2): 559-569, 2010.
  [PDF (preprint)] [Data] [Link] [PubMed]

Papers in conference and workshop proceedings

• [C20] 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.
  [Link]
• [C19] On The Initialization of Long Short-Term Memory Networks
  M.M. Ghazi, M. Nielsen, A. Pai, M. Modat, M.J. Cardoso, S. Ourselin, L. Sørensen, International Conference on Neural Information Processing (ICONIP), 2019.
  [PDF (pre-print)] [Link]
• [W10] Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
  M. Orbes-Arteaga, T. Varsavsky, C.H. Sudre, Z. Eaton-Rosen, L.J. Haddow, L. Sørensen, M. Nielsen, A. Pai, S. Ourselin, M. Modat, P. Nachev, M.J. Cardoso, Domain Adaptation and Representation Transfer (DART), 2019.
  [PDF (pre-print)] [Link]
• [W9] Knowledge Distillation for Semi-Supervised Domain Adaptation
  M. Orbes-Arteaga, M.J. Cardoso, L. Sørensen, C. Igel, S. Ourselin, M. Modat, M. Nielsen, A. Pai, Machine Learning in Clinical Neuroimaging (MLCN), 2019.
  [PDF (pre-print)] [Link]
• [C18] PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
  M. Orbes-Arteaga, L. Sørensen, M.J. Cardoso, M. Modat, S. Ourselin, S. Sommer, M. Nielsen, C. Igel, A. Pai, SPIE Medical Imaging, 2019.
  [PDF (extended abstract)][Link]
• [C17] Robust training of recurrent neural networks to handle missing data for disease progression modeling
  M.M. Ghazi, M. Nielsen, A. Pai, M.J. Cardoso, M. Modat, S. Ourselin, and L. Sørensen, International conference on Medical Imaging with Deep Learning (MIDL), 2018.
  [PDF] [Presentation (YouTube)]
• [C16] Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
  M. Orbes-Arteaga, L. Sørensen, M. Modat, M.J. Cardoso, S. Ourselin, M. Nielsen, and A. Pai, International conference on Medical Imaging with Deep Learning (MIDL), 2018.
  [PDF]
• [W8] A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
  A. Pai, S. Sommer, L.L. Raket, L. Kühnel, S. Darkner, L. Sørensen, and M. Nielsen, Bayesian and Graphical Models for Biomedical Imaging, 2016.
  [PDF (preprint)]
• [C15] Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation
  M. Michalkiewicz, A. Pai, K.K. Leung, S. Sommer, S. Darkner, L. Sørensen, J. Sporring, and M. Nielsen, SPIE Medical Imaging, 2016.
  [Link]
• [W7] Adaptive time-stepping in diffeomorphic image registration with bounded inverse consistency error
  A. Pai, S. Klein, S. Sommer, L. Sørensen, S. Darkner, J. Sporring, and M. Nielsen, MICCAI Workshop on Mathematical Foundations of Computational Anatomy (MFCA), 2015.
  [PDF]
• [C14] Label Stability in Multiple Instance Learning
  V. Cheplygina, L. Sørensen, D.M.J. Tax, M. de Bruijne, and M. Loog, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015.
  [PDF]
• [C13] Image Registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel
  A. Pai, S. Sommer, L. Sørensen, S. Darkner, J. Sporring, and M. Nielsen, SPIE Medical Imaging, 2015.
  [Link]
• [W6] Simultaneous Registration and Bilateral Differential Bias Correction in Brain MRI
  B. Zou, A. Pai, L. Sørensen, and M. Nielsen, Intelligent Imaging: Linking MR Acquisition and Processing, 2014.
  
• [W5] Dementia Diagnosis using MRI Cortical Thickness, Shape, Texture, and Volumetry
  L. Sørensen, A. Pai, C. Anker, I. Balas, M. Lillholm, C. Igel, and M. Nielsen, Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data, 2014.
  [PDF][1st place in challenge]
• [C12] Classification of COPD with Multiple Instance Learning
  V. Cheplygina, L. Sørensen, D.M.J. Tax, J.H. Pedersen, M. Loog, and M. de Bruijne, International Conference on Pattern Recognition (ICPR), 2014.
  [PDF]
• [W4] Stepwise Inverse Consistent Euler's Scheme for Diffeomorphic Image Registration
  A. Pai, S. Sommer, L. Sørensen, S. Darkner, J. Sporring, and M. Nielsen, International Workshop on Biomedical Image Registration (WBIR), 2014.
  [PDF (© Springer, the final publication is available at www.springerlink.com)]
  
• [C11] Cube Propagation for Focal Brain Atrophy Estimation
  A. Pai, L. Sørensen, S. Darkner, P. Mysling, D. Jørgensen, E. Dam, M. Lillholm, J. Oh, G. Chen, J. Suhy, J. Sporring, and M. Nielsen, International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2013.
  [Link]
• [C10] Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI
  L. Lillemark, L. Sørensen, P. Mysling, A. Pai, E. B. Dam, and M. Nielsen, SPIE Medical Imaging, 2013.
  [Link]
• [W3] Evaluation of Bias in Brain Atrophy Estimation,
  A. Pai, S. Darkner, L. Sørensen, L. Lillemark, J. Sporring, E. B. Dam, and M. Nielsen, Novel Neuroimaging Biomarkers for Alzheimer's Disease, 2012.
• [C9] Towards Exaggerated Emphysema Stereotypes
  C. Chen, L. Sørensen, F. Lauze, C. Igel, M. Loog, A. Feragen, M. de Bruijne, and M. Nielsen, SPIE Medical Imaging, 2012.
  [Link]
• [C8] Dissimilarity-Based Classification of Anatomical Tree Structures
  L. Sørensen, P. Lo, A. Dirksen, J. Petersen, and M. de Bruijne, Information Processing in Medical Imaging (IPMI), 2011.
  [PDF (© Springer, the final publication is available at www.springerlink.com)] [PubMed]
  
• [W2] Multiple classifier systems in texton-based approach for the classification of CT images of lung
  M.J. Gangeh, L. Sørensen, S.B Shaker, M.S. Kamel, and M. de Bruijne, Medical Computer Vision 2010: Recognition Techniques and Applications in Medical Imaging, 2010.
  [PDF (© Springer, the final publication is available at www.springerlink.com)]
• [C7] A texton-based approach for the classification of lung parenchyma in CT images
  M.J. Gangeh, L. Sørensen, S.B. Shaker, M.S. Kamel, M. de Bruijne, and M. Loog, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2010.
  [PDF (© Springer, the final publication is available at www.springerlink.com)] [PubMed]
• [C6] Image dissimilarity-based quantification of lung disease from CT
  L. Sørensen, M. Loog, P. Lo, H. Ashraf, A. Dirksen, R.P.W. Duin and M. de Bruijne, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2010.
  [PDF (© Springer, the final publication is available at www.springerlink.com)] [PubMed]
• [C5] Dissimilarity-based multiple instance learning
  L. Sørensen, M. Loog, D.M.J. Tax, W.-J. Lee, M. de Bruijne, and R.P.W Duin, Statistical, Structural and Syntactic Pattern Recognition (S+SSPR), 2010.
  [PDF (© Springer, the final publication is available at www.springerlink.com)]
• [C4] Learning COPD sensitive filters in pulmonary CT
  L. Sørensen, Pechin Lo, H. Ashraf, J. Sporring, M. Nielsen, and M. de Bruijne, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.
  [PDF (© Springer, the final publication is available at www.springerlink.com)] [PubMed]
• [C3] Dissimilarity representations in lung parenchyma classification
  L. Sørensen and M. de Bruijne, SPIE Medical Imaging, 2009.
  [PDF (Copyright 2009 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.)] [link]
  
• [W1] Texture based emphysema quantification in lung CT
  L. Sørensen, S. Shaker, and M. de Bruijne, The First International Workshop on Pulmonary Image Analysis, 2008.
  [PDF]
• [C2] Texture classification in lung CT using local binary patterns
  L. Sørensen, S. Shaker, and M. de Bruijne, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2008.
  [PDF (© Springer, the final publication is available at www.springerlink.com)] [PubMed]
• [C1] Multi-object tracking of human spermatozoa
  L. Sørensen, J. Østergaard, P. Johansen, and M. de Bruijne, SPIE Medical Imaging, 2008.
  [PDF (Copyright 2008 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.)][link]

Abstracts

• [A37] 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, International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), 2021
• [A36] Disease Progression Modeling-Based Prediction of Cognitive Decline
  M.M. Ghazi, M. Nielsen, A. Pai, M. Modat, M.J. Cardoso, S. Ourselin, and L. Sørensen, Alzheimer's Association International Conference (AAIC), 2020 [PDF] [Link]
• [A35] Smart protocol: real-time brain MRI pathology detection by deep learning for online protocol control
  A. Pai, B. Low, L. Sørensen, M. Lillholm, E.B. Dam, R. Lauritzen, R. Kashyape, M. Nielsen, European Congress of Radiology (ECR), 2020 [Link (see RPS 1405a)]
• [A34] MRI Biomarkers Improve Disease Progression Modeling-based Prediction of Cognitive Decline
  M.M. Ghazi, M. Nielsen, A. Pai, M. Modat, M.J. Cardoso, S. Ourselin, and L. Sørensen, Radiological Society of North America (RSNA), 2019.
  [Link]
• [A33] Unsupervised Machine Learning on Baseline Brain MRI Identifies MCI Subgroup with a Faster Decline over Two Years Compared to Classical Hippocampal Sparing AD Subtype
  L. Sørensen, A. Pai, M. Nielsen, J. B. Leverenz, J. A. Pillai, Alzheimer's Association International Conference (AAIC), 2019.
  [Link]
• [A32] Asymmetry in hippocampal texture predicts conversion to AD independent of amyloid status
  J.Z.K. Caldwell, A. Pai, S. Ingala, L. Sørensen, M. Nielsen, J. Kaylegian, S.J. Banks, and J.L. Cummings, The Organization for Human Brain Mapping (OHBM), 2019.
  [Link]
• [A31] Hippocampal Texture in Healthy Cognition and Mild Cognitive Impairment: The Impact of Amyloid Burden and Sex
  J.Z.K. Caldwell, A. Pai, L. Sørensen, J. Kaylegian, J.L. Cummings, and S.J. Banks, Alzheimer's Association International Conference (AAIC), 2018.
  [link]
• [A30] Do you trust your multiple instance learning classifier?
  V. Cheplygina, L. Sørensen, D.M.J. Tax, M. de Bruijne, and M. Loog, The annual machine learning conference of the Benelux Eindhoven (Benelearn), 2017.
  [PDF (proceedings)]
• [A29] Hippocampal Texture Predicts Rate of Cognitive Decline in Mild Cognitive Impairment
  A. Pai, J. Pillai, L. Sørensen, S. Darkner, S. Sommer, M. Nielsen, and J. Leverenz, Alzheimer's Association International Conference (AAIC), 2017.
  [link]
• [A27/A28] MCI Trial Enrichment Using MRI Hippocampus Texture
  L. Sørensen, A. Pai, C. Igel, and M. Nielsen, Alzheimer's Association International Conference (AAIC), 2016 [link] / Alzheimer's Imaging Consortium (AIC), 2016.
• [A26] Does Dementia Biomarkers Have Sigmoid Trajectory? Insights from Non-Linear Mixed Effects Modeling
  A. Pai, S. Sommer, L. L.Raket, L. Sørensen, and M. Nielsen, Alzheimer's Association International Conference (AAIC), 2016.
  [link]
• [A25] Comparison of several computational pipelines for atrophy computation in longitudinal Alzheimers studies
  A. Pai, S. Sommer, S. Darkner, L. Sørensen, J. Sporring, and M. Nielsen, Radiological Society of North America (RSNA), 2015.
  [link]
• [A24] Hippocampus MRI T1 texture's relation to established Alzheimer's disease biomarkers and prediction of progression
  M. Nielsen, L. Sørensen, A. Pai, C. Igel, and M. Lillholm, Radiological Society of North America (RSNA), 2015.
  [link] [Featured study at (RSNA)]
• [A22/A23] Hippocampal MRI Texture Is Related to Hippocampal Glucose Metabolism
  L. Sørensen, A. Pai, C. Igel, and M. Nielsen, Alzheimer's Association International Conference (AAIC) [link] / Alzheimer's Imaging Consortium (AIC), 2015.
• [A21] Improved Alzheimer's disease diagnostic performance using structural MRI: validation of the MRI combination biomarker that won the CADDementia challenge
  L. Sørensen, M. Lillholm, A. Pai, I. Balas, C. Anker, C. Igel, and M. Nielsen, European Congress of Radiology (ECR), 2015.
  [link (ECR 2015 Book of Abstracts)] [poster] [Best scientific paper presentation within Neuro (presented by Martin Lillholm)]
• [A20] Hippocampal Texture Predicts AD Conversion in Amyloid Positive Mild Cognitively Impaired Subjects
  M. Nielsen, C. Igel, and L. Sørensen, International Conference on Alzheimer's and Parkinson's Diseases (AD/PD), 2015.
  [link (full proceedings)]
• [A19] Alzheimer's Disease Diagnostic Performance of a Multi-Atlas Hippocampal Segmentation Method using the Harmonized Hippocampal Protocol
  C. Anker, L. Sørensen, A. Pai, M. Lyksborg, M. Lillholm, K. Conradsen, R. Larsen, and M. Nielsen, Radiological Society of North America (RSNA), 2014.
• [A17/A18] Persistent Hippocampal predominant atrophy in pre dementia as a marker for slower functional decline
  J. Pillai, A. Pai, L. Sørensen, and M. Nielsen, Alzheimer's Association International Conference (AAIC) / Alzheimer's Imaging Consortium (AIC), 2014.
• [A15/A16] Automated Hippocampal Segmentation using new standardized manual segmentations from the Harmonized Hippocampal Protocol
  C. Anker, A. Pai, L. Sørensen, M. Lyksborg, K. Conradsen, R. Larsen, and M. Nielsen, Alzheimer's Association International Conference (AAIC) / Alzheimer's Imaging Consortium (AIC), 2014.
• [A13/A14] White matter hypointensity growth rate correlates with rate of brain atrophy
  A. Pai, L. Sørensen, S. Darkner, J. Sporring, E. Rostrup, and M. Nielsen, Alzheimer's Association International Conference (AAIC) / Alzheimer's Imaging Consortium (AIC), 2014.
• [A11/A12] Validation of Hippocampal Texture for Early Alzheimer's Disease Detection: Generalization to Independent Cohorts and Extrapolation to Very Early Signs of Dementia
  L. Sørensen, C. Igel, N. L. Hansen, M. Lauritzen, M. Osler, E. Rostrup, and M. Nielsen, Alzheimer's Association International Conference (AAIC) [link] / Alzheimer's Imaging Consortium (AIC), 2014.
• [A10] Evaluation of WBAA with Registration-based Cube Propagation for Brain Atrophy Quantification
  M. Lillholm, A. Pai, L. Sørensen, M. Nielsen, J. Sporring, S. Darkner, and E. Dam, Radiological Society of North America (RSNA), 2013
• [A8/A9] Localized Cerebral Atrophy Acceleration in Alzheimer's Disease
  A. Pai, L. Sørensen, S. Darkner, M. Lillholm, E. Dam, J. Oh, G. Chen, J. Suhy, J. Sporring, and M. Nielsen, Alzheimer's Association International Conference (AAIC) / Alzheimer's Imaging Consortium (AIC), 2013
• [A6/A7] Hippocampal Texture Predicts Conversion from MCI to AD
  L. Sørensen, A. Pai, C. Igel, and M. Nielsen, Alzheimer's Association International Conference (AAIC), [link] / Alzheimer's Imaging Consortium (AIC), 2013
• [A5] Hippocampal Texture Predicts One-Year Hippocampal Atrophy in Mild Cognitively Impaired Subjects
  L. Sørensen, A. Pai, P. Mysling, S. Darkner, G. Chen, J. Oh, J. Suhy, C. Igel, and M. Nielsen, European Congress of Radiology (ECR), electronic poster, 2013
  [poster]
• [A4] Hippocampus Atrophy in Early Alzheimer's Disease Quantified Using Non-rigid Registration with Cube Propagation
  A. Pai, L. Sørensen, S. Darkner, P. Mysling, L. L. Larsen, J. Sporring, E. Dam, M. Lillholm, D.R. Jørgensen, G. Chen, J. Oh, J. Suhy, and M. Nielsen, International Conference on Alzheimer's and Parkinson's Diseases (AD/PD), 2013.
• [A2/A3] Hippocampal Texture Provides Volume-Independent Information for Alzheimer's Disease Diagnosis
  L. Sørensen, A. Pai, S. Darkner, J. Suhy, J. Oh, G. Chen, C. Igel, and M. Nielsen, Alzheimer's Association International Conference (AAIC) [errata] / Alzheimer's Imaging Consortium (AIC) [link][errata], 2012.
• [A1] Pattern recognition based emphysema quantification
  L. Sørensen and M. de Bruijne, The 16th Danish Conference on Pattern Recognition and Image Analysis, DIKU technical report no 08-10, 2008.

Book chapters

• Texture classification in pulmonary CT
  L. Sørensen, M.J. Gangeh, S.B. Shaker, and M. de Bruijne, Lung Imaging and Computer Aided Diagnosis, CRC Press, Taylor & Francis Group, August 2011.
  [link] [PDF]

Theses

• Pattern recognition-based analysis of COPD in CT
  L. Sørensen, PhD Thesis, Department of Computer Science, University of Copenhagen, 2010.
  [PDF]
• Multi-object tracking of human spermatozoa using a particle filter
  L. Sørensen, J. Østergaard, MSc Thesis, Department of Computer Science, University of Copenhagen, 2007.

Patents

• [P4] Extraction of a Bias Field Invariant Measure from an Image
  L. Sørensen, M. Nielsen, and C. Igel, Patent Application No. GB1508141.7 filed May 15, 2015, Patent Application No. PCT/EP2016/060787 filed May 12, 2016.
• [P3] Bias Correction in Images
  B. Zou, A. Pai, L. Sørensen, and M. Nielsen, Patent Application No. GB1416416.4 filed October 6, 2014, Patent Application No. US 15/511770 filed March 16, 2017.
• [P2] Computer Based Method for Determining the Size of an Object in an Image
  A. Pai, L. Sørensen, E.B. Dam, M. Lillholm, and M. Nielsen, Patent Application No. US 13/909666 filed April 4, 2013.
• [P1] Classification of medical diagnostic images
  M. de Bruijne, L. Sørensen, and M. Nielsen, Patent Application No. US 13/103656 filed May 9, 2011. U.S. Patent No. US8811724 issued Aug 19, 2014.