Fast predictive image registration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10008 LNCS, Issue , 2016, Pages 48-57

  Yang, Xiao ^a   Kwitt, Roland ^c   Niethammer, Marc ^a,b  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF SALZBURG   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN MAPPING; COMPUTER AIDED ANALYSIS; COMPUTER AIDED INSTRUCTION; DEFORMATION; FORECASTING; IMAGE REGISTRATION; MAPPING; MEDICAL APPLICATIONS; MONTE CARLO METHODS; PARAMETERIZATION; STATISTICAL TESTS; VELOCITY;

DEFORMATION FIELD; DIFFEOMORPHIC TRANSFORMATION; IMAGE APPEARANCE; IMAGE DEFORMATION; LARGE DEFORMATION DIFFEOMORPHIC METRIC MAPPINGS; MONTE CARLO SAMPLING; ORDERS OF MAGNITUDE; PREDICTION ACCURACY;

MEDICAL IMAGING;

EID: 84992512218     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46976-8_6     Document Type: Conference Paper

References (18)

1. Beg, M.F., Miller, M., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. IJCV 61 (2), 139–157 (2005)
2. Cao, T., Singh, N., Jojic, V., Niethammer, M.: Semi-coupled dictionary learning for deformation prediction. In: ISBI, pp. 691–694 (2015)
3. Chou, C.R., Frederick, B., Mageras, G., Chang, S., Pizer, S.: 2D/3D image registration using regression learning. CVIU 117 (9), 1095–1106 (2013)
4. Dosovitskiy, A., Fischery, P., Ilg, E., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: Flownet: learning optical flow with convolutional networks. In: ICCV, pp. 2758–2766 (2015)
5. Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference. arXiv: 1506.02158 (2015)
6. Hart, G.L., Zach, C., Niethammer, M.: An optimal control approach for deformable registration. In: MMBIA, pp. 9–16 (2009)
7. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing humanlevel performance on ImageNet classification. CoRR abs/1502.01852 (2015)
8. Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)
9. Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19 (9), 1498–1507 (2007)
10. Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D/3D registration. TMI 35 (5), 1352–1363 (2016)
11. Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press, Oxford (2004)
12. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
13. Simpson, I.J.A., Woolrich, M. W., Groves, A.R., Schnabel, J.A.: Longitudinal brain MRI analysis with uncertain registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 647–654. Springer, Heidelberg (2011)
14. Singh, N., Hinkle, J., Joshi, S., Fletcher, P.T.: A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction. In: ISBI, pp. 1219–1222 (2013)
15. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15, 1929–1958 (2014)
16. Vialard, F.X., Risser, L., Rueckert, D., Cotter, C.J.: Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. IJCV 97 (2), 229–241 (2012)
17. Wang, Q., Kim, M., Shi, Y., Wu, G., Shen, D.: Predict brain MR image registration via sparse learning of appearance and transformation. MedIA 20 (1), 61–75 (2015)
18. Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: DeepFlow: large displacement optical flow with deep matching. In: ICCV, pp. 1385–1392 (2013)