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.