PUBLICATIONS AND RESEARCH

Selected publications, 2013-2018

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

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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