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Brain Tumor Cell Density Estimation from Multi-modal MR Images Based on a Synthetic Tumor Growth Model.

E. Geremia, B. Menze, M. Prastawa, M. Weber, A. Criminisi, and N. Ayache. MCV, volume 7766 of Lecture Notes in Computer Science, page 273-282. Springer, (2012)

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Antonio Raiolo University of Stuttgart

Giuliano Antonio Giacoppo University of Stuttgart

Rafael Antonio Lainez Reyes University of Stuttgart

Jesus Antonio Martinez Zarate University of Stuttgart

Other publications of authors with the same name

Dense stereo using pivoted dynamic programming.P. Torr, and A. Criminisi. Image Vision Comput., 22 (10): 795-806 (2004)Object Class Segmentation using Random Forests.F. Schroff, A. Criminisi, and A. Zisserman. BMVC, page 1-10. British Machine Vision Association, (2008)Fusion of Stereo, Colour and Contrast.A. Blake, A. Criminisi, G. Cross, V. Kolmogorov, and C. Rother. ISRR, volume 28 of Springer Tracts in Advanced Robotics, page 295-304. Springer, (2005)Assessing Multiple Sclerosis With Kinect: Designing Computer Vision Systems for Real-World Use.C. Morrison, K. Huckvale, R. Corish, J. Dorn, P. Kontschieder, K. O'Hara, A. Criminisi, and A. Sellen. Human-Computer Interaction, 31 (3-4): 191-226 (2016)Filter Forests for Learning Data-Dependent Convolutional Kernels.S. Fanello, C. Keskin, P. Kohli, S. Izadi, J. Shotton, A. Criminisi, U. Pattacini, and T. Paek. CVPR, page 1709-1716. IEEE Computer Society, (2014)A Discriminative-Generative Model for Detecting Intravenous Contrast in CT Images.A. Criminisi, K. Juluru, and S. Pathak. MICCAI (3), volume 6893 of Lecture Notes in Computer Science, page 49-57. Springer, (2011)Image quality transfer and applications in diffusion MRI.D. Alexander, D. Zikic, A. Ghosh, R. Tanno, V. Wottschel, J. Zhang, E. Kaden, T. Dyrby, S. Sotiropoulos, H. Zhang and 1 other author(s). NeuroImage, (2017)Quantifying Registration Uncertainty With Sparse Bayesian Modelling.L. Folgoc, H. Delingette, A. Criminisi, and N. Ayache. IEEE Trans. Med. Imaging, 36 (2): 607-617 (2017)Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements.L. Folgoc, H. Delingette, A. Criminisi, and N. Ayache. Medical Image Analysis, (2017)Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning.A. Criminisi, J. Shotton, and E. Konukoglu. Foundations and Trends in Computer Graphics and Vision, 7 (2-3): 81-227 (2012)