Page 250 - Artificial Intelligence for Computational Modeling of the Heart
P. 250

Bibliography 223





                          Philosophical Transactions of the Royal Society of  263. F.C.Ghesu,B.Georgescu,S.Grbic,A.Maier,J.
                          London. Series A: Mathematical, Physical and  Hornegger, D. Comaniciu, Towards intelligent
                          Engineering Sciences 360 (1792) (2002) 437–451.  robust detection of anatomical structures in
                     253. Santosh Ansumali, Iliya V. Karlin, Hans Christian  incomplete volumetric data, Medical Image
                          Öttinger, Minimal entropic kinetic models for  Analysis 48 (Aug. 2018) 203–213.
                          hydrodynamics, EPL (Europhysics Letters) 63 (6)  264. F.C.Ghesu,B.Georgescu,S.Grbic,A.K. Maier, J.
                          (2003) 798.                                Hornegger, D. Comaniciu, Robust multi-scale
                     254. M.Bouzidi,M.Firdaouss,P.Lallemand,         anatomical landmark detection in incomplete
                          Momentum transfer of a Boltzmann-lattice fluid  3D-CT data, in: Medical Image Computing and
                          with boundaries, Physics of Fluids 13 (11) (2001)  Computer Assisted Intervention, in: LNCS,
                          3452–3459.                                 vol. 10433, Springer International Publishing,
                     255. Guo Zhao-Li, Chu-Guang Zheng, Bao-Chang Shi,  Cham, Switzerland, Sept. 2017, pp. 194–202.
                          Non-equilibrium extrapolation method for  265. F.C. Ghesu, B. Georgescu, T. Mansi, D. Neumann, J.
                          velocity and pressure boundary conditions in the  Hornegger, D. Comaniciu, An artificial agent for
                          lattice Boltzmann method, Chinese Physics 11 (4)  anatomical landmark detection in medical
                          (2002) 366.                                images, in: International Conference on Medical
                     256. Kevin Connington, Qinjun Kang, Hari        Image Computing and Computer-Assisted
                          Viswanathan, Amr Abdel-Fattah, Shiyi Chen,  Intervention, Springer, 2016, pp. 229–237.
                          Peristaltic particle transport using the lattice  266. R.S. Sutton, A.G. Barto, Reinforcement Learning:
                          Boltzmann method, Physics of Fluids 21 (5) (2009)  An Introduction, vol. 1, MIT Press, Cambridge,
                          053301.                                    1998.
                     257. F.C. Ghesu, E. Krubasik, B. Georgescu, V. Singh, Y.  267. C.J.C.H. Watkins, P. Dayan, Q-learning, Machine
                          Zheng, J. Hornegger, D. Comaniciu, Marginal  Learning 8 (May 1992) 279–292.
                          space deep learning: efficient architecture for  268. C.J.C.H. Watkins, Learning From Delayed
                          volumetric image parsing, IEEE Transactions on  Rewards, PhD thesis, King’s College, Cambridge,
                          Medical Imaging 35 (May 2016) 1217–1228.
                     258. F.C.Ghesu,B.Georgescu,Y.Zheng,J.Hornegger,  England, May 1989.
                          D. Comaniciu, Marginal space deep learning:  269. T. Lindeberg, Scale-Space Theory in Computer
                          efficient architecture for detection in volumetric  Vision, Kluwer Academic Publishers, Norwell, MA,
                          image data, in: Medical Image Computing and  USA, 1994.
                          Computer-Assisted Intervention, in: LNCS,  270. R. Bellman, Dynamic Programming, Princeton
                          vol. 9349, Springer International Publishing,  University Press, 1957.
                                                                 271. P.H.S. Torr, A. Zisserman, MLESAC: a new robust
                          Cham, Switzerland, Oct. 2015, pp. 710–718.
                                                                     estimator with application to estimating image
                     259. P. Viola, M. Jones, Rapid object detection using a
                          boosted cascade of simple features, in:    geometry, Computer Vision and Image
                          Proceedings of the IEEE Conference on Computer  Understanding 78 (Apr. 2000) 138–156.
                          Vision and Pattern Recognition, IEEE Computer  272. X.Lu, B. Georgescu, M.-P.Jolly,J.Guehring, A.
                          Society, 2001, pp. 511–518.                Young, B. Cowan, A. Littmann, D. Comaniciu,
                     260. F.C.Ghesu,B.Georgescu,J.Hornegger, Deep    Cardiac anchoring in MRI through context
                          Learning for Medical Image Analysis, Ch. Efficient  modeling, in: T. Jiang, N. Navab, J.P.W. Pluim, M.A.
                          medical image parsing, Academic Press, New York,  Viergever (Eds.), MICCAI 2010, Part I, in: LNCS,
                          USA, Jan. 2017, pp. 55–81.                 vol. 6361, Springer, Heidelberg, 2010, pp. 383–390.
                     261. V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J.  273. X. Lu, M.-P. Jolly, Discriminative context modeling
                          Veness, M.G. Bellemare, A. Graves, M. Riedmiller,  using auxiliary markers for LV landmark detection
                          A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie,  from a single MR image, in: O. Camara, T. Mansi,
                          A. Sadik, I. Antonoglou, H. King, D. Kumara, D.  M. Pop, K. Rhode, M. Sermesant, A. Young (Eds.),
                          Wierstra, S. Legg, D. Hassabis, Human-level  TACOM 2013, in: LNCS, vol. 7746, Springer,
                          control through deep reinforcement learning,  Heidelberg, 2013, pp. 105–114.
                          Nature 518 (7540) (2015) 529–533.      274. J. Long, E. Shelhamer, T. Darrell, Fully
                     262. F.C.Ghesu,B.Georgescu,Y.Zheng,S.Grbic,A.   convolutional networks for semantic
                          Maier, J. Hornegger, D. Comaniciu, Multi-scale  segmentation, in: IEEE CVPR, 2015, pp. 3431–3440.
                          deep reinforcement learning for real-time  275. Ö. Çiçek, A. Abdulkadir, S.S. Lienkamp, T. Brox, O.
                          3D-landmark detection in CT scans, IEEE    Ronneberger, 3d U-Net: learning dense volumetric
                          Transactions on Pattern Analysis and Machine  segmentation from sparse annotation, in:
                          Intelligence 41 (1) (2017) 176–189.        MICCAI, Springer, 2016, pp. 424–432.
   245   246   247   248   249   250   251   252   253   254   255