PUBLICATIONS AND RESEARCH

Selected publications, 2013-2019

2020

Tensor Networks for Medical Image Classification
R Selvan, EB Dam
2020 Conference: Medical Imaging with Deep Learning

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, JH Pedersen, A Dirksen, M de Bruijne American Journal of Roentgenology, 1-112020

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, …arXiv preprint arXiv:2002.03419

2019

Medial Cartilage Surface Integrity as a Surrogate Measure for Incident Radiographic Knee Osteoarthritis following Weight Changes
Jos Runhaar, Erik B Dam, Edwin HG Oei, Sita MA Bierma-Zeinstra
2019 Journal: Cartilage

Impact of adding breast density to breast cancer risk models: A systematic review
Bolette Mikela Vilmun, Ilse Vejborg, Elsebeth Lynge, Martin Lillholm, Mads Nielsen, Michael Bachmann Nielsen, Jonathan Frederik Carlsen

Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark
M von Euler-Chelpin, M Lillholm, I Vejborg, M Nielsen, E Lynge (2019)
Breast Cancer Research 21 (1), 111

Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy: 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in …
D Zhu, J Yan, H Huang, L Shen, PM Thompson, CF Westin, X Pennec, …
Springer Nature

One network to segment them all: A general, lightweight system for accurate 3d medical image segmentation
Mathias Perslev, Erik Bjørnager Dam, Akshay Pai, Christian Igel (2019/10/13) Læs artiklen her
Conference: International Conference on Medical Image Computing and Computer-Assisted Intervention.

Multi-domain adaptation in brain MRI through paired consistency and adversarial learning
Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H Sudre, Zach Eaton-Rosen, Lewis J Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M Jorge Cardoso (2019/10/13) Læs artiklen her
Book: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data.

On the Initialization of Long Short-Term Memory Networks
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, Marc Modat, M Jorge Cardoso, Sébastien Ourselin, Lauge Sørensen (2019/12/12) Læs artiklen her
International Conference on Neural Information Processing

Robust parametric modeling of Alzheimer’s disease progression
MM Ghazi, M Nielsen, A Pai, M Modat, MJ Cardoso, S Ourselin, …
arXiv preprint arXiv:1908.05338

Knowledge distillation for semi-supervised domain adaptation
M Orbes-Arteaga, J Cardoso, L Sørensen, C Igel, S Ourselin, M Modat, …
arXiv preprint arXiv:1908.07355

Validation of an open source, generic deep learning architecture for 3D MRI segmentation-with data from the OAI, PROOF, and CCBR
M Perslev, A Pai, C Igel, J Runhaar, EB Dam
Osteoarthritis and Cartilage 27, S393-S394

Confidence Measures for Deep Learning in Domain Adaptation
S Bonechi, P Andreini, M Bianchini, A Pai, F Scarselli
Applied Sciences 9 (11), 2192

The Value of Hippocampal Volume, Shape and Texture for 11-year Prediction of Dementia: a Population-Based Study
HC Achterberg, L Sørensen, FJ Wolters, WJ Niessen, MW Vernooij, …
Neurobiology of Aging

The Value of Hippocampal Volume, Shape and Texture for 11-year Prediction of Dementia: a Population-Based Study
HC Achterberg, L Sørensen, FJ Wolters, WJ Niessen, MW Vernooij, …
Neurobiology of Aging

Training recurrent neural networks robust to incomplete data: Application to Alzheimer’s disease progression modeling
MM Ghazi, M Nielsen, A Pai, MJ Cardoso, M Modat, S Ourselin, …
Medical image analysis 53, 39-46. Læs artiklen her

PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
M Orbes-Arteaga, L Sørensen, J Cardoso, M Modat, S Ourselin, …
Medical Imaging 2019: Image Processing 10949, 109490S

Change in mammographic density across birth cohorts of Dutch breast cancer screening participants
G Napolitano, E Lynge, M Lillholm, I Vejborg, CH van Gils, M Nielsen, …
International journal of cancer

AI vil give store samfundsgevinster
PB Brockhoff, M Nielsen, J Damsgaard
Børsen, 5

Increasing Accuracy of Optimal Surfaces Using Min-marginal Energies
J Petersen, AM Arias-Lorza, R Selvan, D Bos, A van der Lugt, …
IEEE transactions on medical imaging.

2018

Collocation for Diffeomorphic Deformations in Medical Image Registration.
Darkner, S., Pai, A., Liptrot, M.G., and Sporring, J. (2018). 
IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 1570–1583.

Robust training of recurrent neural networks to handle missing data for disease progression modeling.
Ghazi, M.M., Nielsen, M., Pai, A., Cardoso, M.J., Modat, M., Ourselin, S., and Sørensen, L. (2018).
In proceedings of Medical Imaging with Deep Learning 2018.

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs.
Orbes-Arteaga, M., Cardoso, M.J., Sørensen, L., Modat, M., Ourselin, S., Nielsen, M., and Pai, A. (2018). 
ArXiv:1808.06519 [Cs].

Subclinical depressive symptoms during late midlife and structural brain alterations: A longitudinal study of Danish men born in 1953.
Osler, M., Sørensen, L., Rozing, M., Calvo, O.P., Nielsen, M., and Rostrup, E. (2018).
Human Brain Mapping 39, 1789–1795.

Boundary Optimizing Network (BON).
Singh, M., and Pai, A. (2018).
ArXiv:1801.02642 [Cs, Stat].

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.
Sørensen, L., and Nielsen, M. (2018).
Journal of Neuroscience Methods 302, 66–74

Screening mammography: benefit of double reading by breast density
M von Euler-Chelpin, M Lillholm, G Napolitano, I Vejborg, M Nielsen, …
Breast cancer research and treatment 171 (3), 767-776. Læs artiklen her

On variational methods for motion compensated inpainting
F Lauze, M Nielsen
arXiv preprint arXiv:1809.079833 Læs artiklen her

Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
M Orbes-Arteaga, MJ Cardoso, L Sørensen, M Modat, S Ourselin, …
arXiv preprint arXiv:1808.06519

Boundary Optimizing Network (BON)
M Singh, A Pai
arXiv preprint arXiv:1801.02642

Knee Segmentation by Multiplanar Deep Learning Network-withdatafromOAI
M Perslev, ASU Pai, C Igel, EB Dam
12th International Workshop on Osteoarthritis Imaging

2017

Most Likely Separation of Intensity and Warping Effects in Image Registration.
Kühnel, L., Sommer, S., Pai, A., and Raket, L. (2017). 
SIAM J. Imaging Sci. 10, 578–601.

Deep-learnt classification of light curves.
Mahabal, A., Sheth, K., Gieseke, F., Pai, A., Djorgovski, S.G., Drake, A.J., and Graham, M.J. (2017). 
In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8.

Chapter 10 – Characterization of Errors in Deep Learning-Based Brain MRI Segmentation.
Pai, A., Teng, Y.-C., Blair, J., Kallenberg, M., Dam, E.B., Sommer, S., Igel, C., and Nielsen, M. (2017a).
In Deep Learning for Medical Image AnalysisS.K. Zhou, H. Greenspan, and D. Shen, eds. (Academic Press), pp. 223–242.

A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images.
Pai, A., Sommer, S., Raket, L.L., Kühnel, L., Darkner, S., Sørensen, L., and Nielsen, M. (2017b).
In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging.
Springer International Publishing, pp. 151–159.

Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry.
Sørensen, L., Igel, C., Pai, A., Balas, I., Anker, C., Lillholm, M., and Nielsen, M. (2017).
NeuroImage: Clinical 13, 470–482.

Classification of Alzheimer and MCI Phenotypes on MRI Data Using SVM
KR Kruthika, Akshay Pai, HD Maheshappa (2017).
International Symposium on Signal Processing and Intelligent Recognition Systems.

SIRS 2017: Advances in Signal Processing and Intelligent Recognition Systems pp 263-275

A Stochastic Large Deformation Model for Computational Anatomy
By A. Arnaudon, D.D. Holm and A. Pai , S. Sommer (2017)
In proceedings of International Conference on Information Processing in Medical Imaging.
CSGB Research Reports No. 02, March 2017

2016

Supervised hub-detection for brain connectivity.
Kasenburg, N., Liptrot, M., Reislev, N.L., Garde, E., Nielsen, M., and Feragen, A. (2016).
In Medical Imaging 2016: Image Processing,(International Society for Optics and Photonics), p. 978409.

Combining the boundary shift integral and tensor-based morphometry for brain atrophy estimation.
Michalkiewicz, M., Pai, A., Leung, K.K., Sommer, S., Darkner, S., Sørensen, L., Sporring, J., and Nielsen, M. (2016).
In Medical Imaging 2016: Image Processing, (International Society for Optics and Photonics), p. 978406.

Deformation-based atrophy computation by surface propagation and its application to Alzheimer’s disease.
Pai, A., Sporring, J., Darkner, S., Dam, E.B., Lillholm, M., Jørgensen, D., Oh, J., Chen, G., Suhy, J., Sørensen, L., et al. (2016a).
JMI, JMIOBU 3, 014005.

Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions.
Pai, A., Sommer, S., Sorensen, L., Darkner, S., Sporring, J., and Nielsen, M. (2016b).
IEEE Transactions on Medical Imaging 35, 1369–1380

Early detection of Alzheimer’s disease using MRI hippocampal texture.
Sørensen, L., Igel, C., Hansen, N.L., Osler, M., Lauritzen, M., Rostrup, E., and Nielsen, M. (2016).
Human Brain Mapping 37, 1148–1161.

2015

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge.
Bron, E.E., Smits, M., van der Flier, W.M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J.M., Steketee, R.M.E., Méndez Orellana, C., Meijboom, R., et al. (2015).
NeuroImage 111, 562–579.

Locally Orderless Registration for Diffusion Weighted Images.
Jensen, H.G., Lauze, F., Nielsen, M., and Darkner, S. (2015a).
MICCAI 2015, N. Navab, J. Hornegger, W.M. Wells, and A. Frangi, eds. (Springer International Publishing), pp. 305–312.

Multimodal Brain Extraction from Structural MRI using Co-registered FDG-PET.
Jensen, H.G., Federspiel, F., Ptito, M., Nielsen, M., Gjedde, A., Keller, S.H., Law, I., Kupers, R., and Darkner, S. (2015b).
In proceedings of Computational Methods for Molecular Imaging (CMMI) 2015.

Improved Alzheimer’s disease diagnostic performance using structural MRI:
Nielsen, M. (2015). 
Validation of the MRI combination biomarker that won the CADDementia challenge.

Image registration using stationary velocity fields parameterized by norm-minimizing Wendland kernel.
Pai, A., Sommer, S., Sørensen, L., Darkner, S., Sporring, J., and Nielsen, M. (2015a).
In Medical Imaging 2015: Image Processing, (International Society for Optics and Photonics), p. 941335.

Diffeomorphic image registration with automatic time-step adjustment.
Pai, A., Klein, S., Sommer, S., Darkner, S., Sporring, J., and Nielsen, M. (2015b).
In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), (Brooklyn, NY, USA: IEEE), pp. 1085–1088.

Adaptive time-stepping in dieomorphic image registration with bounded inverse consistency error.
Pai, A., Klein, S., Sommer, S., Sørensen, L., Sporring, J., and Nielsen, M.
The 18th International Conference on Medical Image Computing and Computer Assisted Intervention: proceedings. Technische Universität München , 2015. s. 35-47.

2014

Brain region’s relative proximity as marker for Alzheimer’s disease based on structural MRI.
Lillemark, L., Sørensen, L., Pai, A., Dam, E.B., Nielsen, M., and Alzheimer’s Disease Neuroimaging Initiative (2014). 
BMC Medical Imaging 14, 21.

Stepwise Inverse Consistent Euler’s Scheme for Diffeomorphic Image Registration.
Pai, A., Sommer, S., Darkner, S., Sørensen, L., Sporring, J., and Nielsen, M. (2014b).
In Biomedical Image Registration, Springer International Publishing, pp. 223–230.

Dementia Diagnosis using MRI Cortical Thickness, Shape, Texture, and Volumetry.
Sørensen, L., Pai, A., Anker, C., Balas, I., Lillholm, M., Igel, C., and Nielsen, M.
MICCAI 2014 Workshop Proceedings: Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data. 2014. p. 111-118.

2013

Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI.
Lillemark, L., Sørensen, L., Mysling, P., Pai, A., Dam, E.B., and Nielsen, M. (2013).
In Medical Imaging 2013: Image Processing, (International Society for Optics and Photonics), p. 866926.

Cube propagation for focal brain atrophy estimation.
Pai, A., Sorensen, L., Darkner, S., Mysling, P., Jorgensen, D., Dam, E.B., Lillholm, M., Oh, J., Chen, G., Suhy, J., et al. (2013).
In 2013 IEEE 10th International Symposium on Biomedical Imaging, (San Francisco, CA: IEEE), pp. 402–405.

Mathematical Methods for Medical Imaging.
Pennec, X., Joshi, S., and Nielsen, M. (2013).
Int J Comput Vis 105, 109–110.

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network.
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., and Nielsen, M. (2013).
In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, K. Mori, I. Sakuma, Y. Sato, C. Barillot, and N. Navab, eds. (Springer Berlin Heidelberg), pp. 246–253.

Higher-Order Momentum Distributions and Locally Affine LDDMM Registration.
Sommer, S., Nielsen, M., Darkner, S., and Pennec, X. (2013).
SIAM J. Imaging Sci. 6, 341–367.

Sparse multi-scale diffeomorphic registration : The Kernel Bundle Framework.
Sommer, Stefan Horst; Lauze, Francois Bernard; Nielsen, Mads; Pennec, Xavier.
Journal of Mathematical Imaging and Vision, Vol. 46, Nr. 3, 2013, s. 292-308.

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