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CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN

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Medical Image Understanding and Analysis (MIUA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1248))

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Abstract

This paper reports on a new CT volume registration method, using 3D Convolutional Neural Networks (CNN). The proposed method uses the Least Square Generative Adversarial Network (LSGAN) model consisting of the Contraction-Expansion registration network as the LSGAN’s generator and a deep 3D CNN classification network as the LSGAN’s discriminator. The training of the generator is performed first on its own, using Charbonnier and smoothness loss functions, with progressive weights update moving from lower to higher resolution layers of the Expander. Subsequently, the complete network (Contraction-Expansion with the Discriminator) is trained as a LSGAN network. For the training, CREATIS and COPDgene datasets have been used in a self-supervised paradigm, using 3D warping of the moving volume to estimate the error with respect to the reference volume. The input to the network has 256 × 256 × 128 × 2 voxels and the output is displacement field of 128 × 128 × 64 × 3 voxels. The Contraction-Expansion registration network, on its own, achieves mean error of 1.30 mm with 1.70 standard deviation (SD) on the DIR-LAB dataset. When the whole proposed LSGAN network is used, the mean error is further reduced to 1.13 mm with 0.67 (SD). Therefore, the use of the GAN paradigm reduces the mean error by approximately 15%, providing the state-of-the-art performance.

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References

  1. Sentker, T., Madesta, F., Werner, R.: GDL-FIRE4D: deep learning-based fast 4D CT image registration. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 765–773. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_86

    Chapter  Google Scholar 

  2. Rosu, M., Hugo, G.D.: Advances in 4D radiation therapy for managing respiration: part II–4D treatment planning. Zeitschrift für Medizinische Physik. 22(4), 272–280 (2012)

    Article  Google Scholar 

  3. Yamamoto, T., Kabus, S., Bal, M., Keall, P., Benedict, S., Daly, M.: The first patient treatment of computed tomography ventilation functional image-guided radiotherapy for lung cancer. Radiother. Oncol. 118(2), 227–231 (2016)

    Article  Google Scholar 

  4. Eppenhof, K.A., Pluim, J.P.: Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks. J. Med. Imaging 5(2), 024003 (2018)

    Article  Google Scholar 

  5. Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration–a deep learning approach. NeuroImage 158, 378–396 (2017)

    Article  Google Scholar 

  6. Hu, Y., et al.: Adversarial deformation regularization for training image registration neural networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 774–782. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_87

    Chapter  Google Scholar 

  7. Yan, P., Xu, S., Rastinehad, A.R., Wood, B.J.: Adversarial image registration with application for MR and TRUS image fusion. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 197–204. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00919-9_23

    Chapter  Google Scholar 

  8. Qin, C., Shi, B., Liao, R., Mansi, T., Rueckert, D., Kamen, A.: Unsupervised deformable registration for multi-modal images via disentangled representations. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 249–261. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_19

    Chapter  Google Scholar 

  9. Tanner, C., Ozdemir, F., Profanter, R., Vishnevsky, V., Konukoglu, E., Goksel, O.: Generative adversarial networks for MR-CT deformable image registration. arXiv preprint arXiv:1807.07349, 19 July 2018

  10. Mahapatra, D., Sedai, S., Garnavi, R.: Elastic registration of medical images with GANs. arXiv preprint arXiv:1805.02369, 7 May 2018

  11. Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226–236 (2019)

    Article  Google Scholar 

  12. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059, 11 June 2016

    Google Scholar 

  13. Alansary, A., et al.: Evaluating reinforcement learning agents for anatomical landmark detection. Med. Image Anal. 53, 156–164 (2019)

    Article  Google Scholar 

  14. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 4 September 2014

  15. Barron, J.T.: A general and adaptive robust loss function. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4331–4339 (2019)

    Google Scholar 

  16. Jaderberg, M., Simonyan, K., Zisserman, A.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)

    Google Scholar 

  17. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802 (2017)

    Google Scholar 

  18. Vandemeulebroucke, J., Bernard, O., Rit, S., Kybic, J., Clarysse, P., Sarrut, D.: Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT. Med. Phys. 39(2), 1006–1015 (2012)

    Article  Google Scholar 

  19. Castillo, R., et al.: A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive. Phys. Med. Biol. 58(9), 2861 (2013)

    Article  Google Scholar 

  20. Castillo, R., et al.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54(7), 1849 (2009)

    Article  Google Scholar 

  21. Papież, B.W., Heinrich, M.P., Fehrenbach, J., Risser, L., Schnabel, J.A.: An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration. Med. Image Anal. 18(8), 1299–1311 (2014)

    Article  Google Scholar 

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Correspondence to Essa R. Anas .

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Anas, E.R., Onsy, A., Matuszewski, B.J. (2020). CT Scan Registration with 3D Dense Motion Field Estimation Using LSGAN. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_16

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  • DOI: https://doi.org/10.1007/978-3-030-52791-4_16

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