Journal Publications 2019

  1. Arnaudon, A., Holm, D.D., & Sommer, S. (2019): A geometric framework for stochastic shape analysis. Found. Comput. Math. 19, 653-701.
  2. Arnspang, E.C., Sengupta, P., Mortensen, K.I., Jensen, H.H., Hahn, U., Jensen, E.B.V., Lippincott-Schwartz, J. and Nejsum, L.N. (2019): Regulation of plasma membrane nanodomains of the water channel aquaporin-3 revealed by fixed and live photoactivated localization microscopy. Nano Lett. 19, 699-707.
  3. Baddeley, A. Rubak, E. & Turner, R. (2019): Leverage and influence diagnostics for Gibbs spatial point processes. Spat. Stat. 29, 15-48.
  4. Biscio, C.A.N. & Møller, J. (2019): The accumulated persistence function, a new useful functional summary statistic for topological data analysis, with a view to brain artery trees and spatial point process applications. J. Comput. Graph. Stat. 28, 671-681.
  5. Biscio, C.A.N. & Waagepetersen, R. (2019): A general central limit theorem and subsampling variance estimator for α-mixing point processes. Scand. J. Stat. 46, 1168-1190.
  6. Coeurjolly, J.F., Cuevas-Pacheco, F. & Waagepetersen, R. (2019): Second-order variational equations for spatial point processes with a view to pair correlation function estimation. Spat. Stat. 30, 103-115.
  7. Dela Haije, T. & Feragen, A. (2019): Optimized response function estimation for spherical deconvolution. International MICCAI Workshop on Computational Diffusion MRI 2019.
  8. Dela-Haije, T., Savadjiev, P., Fuster, A., Schultz, R.T., Verma, R., Florack, L. & Westin, C. (2019): Structural connectivity analysis using Finsler geometry. SIAM J. Imaging Sci. 12, 551-575.
  9. Dela Haije, T., Özarslan, E. & Feragen, A. (2019): Enforcing necessary non-negativity constraints for common diffusion MRI models using sum of squares programming. NeuroImage, in press. DOI: 10.1016/j.neuroimage.2019.116405.
  10. Hansen, J.D.K. & Lauze, F. (2019): Segmentation of 2D and 3D objects with intrinsically similarity invariant shape regularisers. Proceedings of the 7th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2019). Lecture Notes in Computer Science 11603, pp. 369-380. Springer.
  11. Hasselholt, S., Hahn, U., Jensen, E.B.V. & Nyengaard, J.R. (2019): Practical implementation of the planar and spatial rotator in a complex tissue: the brain. J. Microsc. 273, 26-35.
  12. Holm, A.N., Feragen, A., Dela Haije, T. & Darkner, S. (2019): Deterministic group tractography with local uncertainty quantification. Proceedings of the International MICCAI Workshop on Computational Diffusion MRI 2018, pp. 377-386.
  13. Jalilian, A., Guan, Y. & Waagepetersen, R. (2019): Orthogonal series estimation of the pair correlation function of a spatial point process. Stat. Sinica 29, 769-787.
  14. Jørgensen, C.H.L., Møller, J.G., Sommer, S. & Johannsson, H. (2019): A memory-efficient parallelizable method for computation of Thévenin equivalents used in real-time stability. IEEE T. Power Syst. 34, 2675-2684.
  15. Khan, A.R., Hansen, B., Danladi, J., Chuhutin, A., Wiborg, O., Nyengaard, J.R. & Jepsersen, S. (2019): Neurite atrophy in dorsal hippocampus of rat indicates incomplete recovery of chronic mild stress induced depression: neurite atrophy in the hippocampus show partial recovery of depression. NMR Biomed. 32, 1-12.
  16. Kok Nielsen, R., Darkner, S. & Feragen, A. (2019): TopAwaRe: Topology-aware registration. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2019, pp. 364-372.
  17. Kühnel, L., Sommer, S. & Arnaudon, A. (2019): Differential geometry and stochastic dynamics with deep learning numerics. Appl. Math. Comput. 356, 411-437.
  18. Larsen, N.Y., Ziegel, J.F., Nyengaard, J.R. & Jensen, E.B.V. (2019): Stereological estimation of particle shape from vertical sections. J. Microsc. 275, 183-194.
  19. Mallasto, A., Hauberg, S. & Feragen, A. (2019): Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models. Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). Machine Learning Research (PMLR) 89, 2368-2377.
  20. Mehdi Moradi, M., Cronie, O., Rubak, E., Lachièze-Rey, R., Mateu, J. & Baddeley, A. (2019): Resample-smoothing of Voronoi intensity estimators. Stat. Comput. 29, 995–1010.
  21. Musazzi, L., Sala, N., Tornese, P., Gallivanone, F., Belloli, S., Conte, A., Di Grigoli, G., Chen, F., Ikinci, A., Treccani, G., Bazzini, C., Castiglioni, I., Nyengaard, J.R., Wegener, G., Moresco, R.M. & Popoli, M. (2019): Acute inescapable stress rapidly increases synaptic energy metabolism in prefrontal cortex and alters working memory performance. Cereb. Cortex 29, 4948-4957.
  22. Pennec, X., Sommer, S. & Fletcher, T. (2019, eds.): Riemannian Geometric Statistics in Medical Image Analysis, Elsevier.
  23. Petersen, J., Arias-Lorza, A.M., Selvan, R., Bos, D., van der Lugt, A., Pedersen, J.H., Nielsen. M. & de Bruijne, M. (2019): Increasing accuracy of optimal surfaces using min-marginal energies. IEEE T. Med. Imaging 38, 1559-1568.
  24. Rønn-Nielsen, A. and Jensen, E.B.V. (2019): Central limit theorem for mean and variogram estimators in Lévy-based models. J. Appl. Probab. 56, 209-222.
  25. Sommer, S. (2019): An infinitesimal probabilistic model for principal component analysis of manifold valued data. Sankhya A 81, 37-62.
  26. Sommer, S., Aabrandt, A. & Jóhannsson, H. (2019): Reduce-factor-solve for fast Thévenin impedance computation and network reduction. IET Gener. Transm. Dis. 13, 288-295.
  27. Sporring, J., Waagepetersen, R.P. & Sommer, S.H. (2019): Generalizations of Ripley’s K-function with application to space curves. Proceedings of the International Conference on Image Processing in Medical Imaging (IPMI 2019). Lecture Notes in Computer Science 11492, pp. 731-742. Springer.
  28. Svane, H. & Feragen, A. (2019): Reconstruction of objects from noisy images at low resolution. Proceedings of the 12th International Workshop on Graph-based Representations in Pattern Recognition. Lecture Notes in Computer Science 11510, pp. 204-214. Springer.
  29. Treccani, G., Ardalan, M., Chen, F., Musazzi, L., Popoli, M., Wegener, G., Nyengaard, J.R. & Müller, H.K. (2019): S-ketamine reverses hippocampal dendritic spine deficits in Flinders Sensitive Line rats within 1h of administration. Mol. Neurobiol. 56, 7368-7379.
  30. Xu, G., Waagepetersen, R. & Guan, Y. (2019): Stochastic quasi-likelihood for case-control point pattern data, J. Am. Stat. Assoc. 114, 631-644.