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@@ -9,8 +9,9 @@ RegNet
 In this paper we propose a method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. We design a Convolutional Neural Network (CNN) architecture that, in contrast to all other work, directly estimates the displacement vector field (DVF) from a pair of input images. The proposed RegNet is trained using a large set of artificially generated DVFs, does not explicitly define a dissimilarity metric, and integrates image content at multiple scales to equip the network with contextual information. At testing time nonrigid registration is performed in a single shot, in contrast to current iterative methods.
 
 ### Citation
+[1] Sokooti, H., de Vos, B., Berendsen, F., Ghafoorian, M., Yousefi, S., Lelieveldt, B.P., Isgum, I. and Staring, M., 2019. 3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations. arXiv preprint arXiv:1908.10235.
 
-[1] Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P., Išgum, I. and Staring, M., 2017, September. Nonrigid image registration using multi-scale 3D convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 232-239). Springer, Cham.
+[2] Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P., Išgum, I. and Staring, M., 2017, September. Nonrigid image registration using multi-scale 3D convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 232-239). Springer, Cham.
 
 	
 ## 1. Dependencies