Infusing Physically Inspired Known Operators in Deep Models of Ultrasound Elastography

Author:

K. Z. Tehrani Ali,Rivaz Hassan

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Delaunay, R., Hu, Y., Vercauteren, T.: An unsupervised approach to ultrasound elastography with end-to-end strain regularisation. In: Martel, A.L., et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III, pp. 573–582. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59716-0_55

2. Guo, L., Xu, Y., Xu, Z., Jiang, J.: A PDE-based regularization algorithm toward reducing speckle tracking noise: A feasibility study for ultrasound breast elastography. Ultrason. Imaging 37(4), 277–293 (2015)

3. Hur, J., Roth, S.: Iterative residual refinement for joint optical flow and occlusion estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5754–5763 (2019)

4. Tehrani, A.K.Z., Rivaz, H.: MPWC-Net++: evolution of optical flow pyramidal convolutional neural network for ultrasound elastography. In: Medical Imaging 2021: Ultrasonic Imaging and Tomography, vol. 11602, p. 1160206. International Society for Optics and Photonics (2021)

5. Tehrani, A.K.Z., Rivaz, H.: Physically inspired constraint for unsupervised regularized ultrasound elastography. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV, pp. 218–227. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16440-8_21

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