Advertisement

JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-modal Image Alignment of Large-Scale Pathological CT Scans

Conference paper
  • 521 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12358)

Abstract

Multi-modal image registration is a challenging problem that is also an important clinical task for many real applications and scenarios. As a first step in analysis, deformable registration among different image modalities is often required in order to provide complementary visual information. During registration, semantic information is key to match homologous points and pixels. Nevertheless, many conventional registration methods are incapable in capturing high-level semantic anatomical dense correspondences. In this work, we propose a novel multi-task learning system, JSSR, based on an end-to-end 3D convolutional neural network that is composed of a generator, a registration and a segmentation component. The system is optimized to satisfy the implicit constraints between different tasks in an unsupervised manner. It first synthesizes the source domain images into the target domain, then an intra-modal registration is applied on the synthesized images and target images. The segmentation module are then applied on the synthesized and target images, providing additional cues based on semantic correspondences. The supervision from another fully-annotated dataset is used to regularize the segmentation. We extensively evaluate JSSR on a large-scale medical image dataset containing 1,485 patient CT imaging studies of four different contrast phases (i.e., 5,940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks. The performance is improved after joint training on the registration and segmentation tasks by \(0.9\%\) and \(1.9\%\) respectively compared to a highly competitive and accurate deep learning baseline. The registration also consistently outperforms conventional state-of-the-art multi-modal registration methods.

Notes

Acknowledgements

This work was partially supported by the Lustgarten Foundation for Pancreatic Cancer Research. The main work was done when F. Liu was a research Intern at PAII Inc. We thank Zhuotun Zhu and Yingda Xia for instructive discussions.

References

  1. 1.
    Arar, M., Ginger, Y., Danon, D., Bermano, A.H., Cohen-Or, D.: Unsupervised multi-modal image registration via geometry preserving image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13410–13419 (2020)Google Scholar
  2. 2.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  3. 3.
    Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)CrossRefGoogle Scholar
  4. 4.
    Blendowski, M., Heinrich, M.P.: Learning interpretable multi-modal features for alignment with supervised iterative descent. In: International Conference on Medical Imaging with Deep Learning, pp. 73–83 (2019)Google Scholar
  5. 5.
    Cao, X., Yang, J., Gao, Y., Guo, Y., Wu, G., Shen, D.: Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis. Med. Image Anal. 41, 18–31 (2017)CrossRefGoogle Scholar
  6. 6.
    Harrison, A.P., Xu, Z., George, K., Lu, L., Summers, R.M., Mollura, D.J.: Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 621–629. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66179-7_71
  7. 7.
    Heinrich, M., Maier, O., Handels, H.: Multi-modal multi-atlas segmentation using discrete optimisation and self-similarities. CEUR Workshop Proceedings (2015)Google Scholar
  8. 8.
    Heinrich, M.P., et al.: MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16(7), 1423–1435 (2012)CrossRefGoogle Scholar
  9. 9.
    Heinrich, M.P., Jenkinson, M., Brady, S.M., Schnabel, J.A.: Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 115–122. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33454-2_15CrossRefGoogle Scholar
  10. 10.
    Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imaging 32(7), 1239–1248 (2013)CrossRefGoogle Scholar
  11. 11.
    Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40811-3_24CrossRefGoogle Scholar
  12. 12.
    Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1–13 (2018)CrossRefGoogle Scholar
  13. 13.
    Huang, X., Liu, M.Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 172–189 (2018)Google Scholar
  14. 14.
    Huo, Y., et al.: SynSeg-Net: synthetic segmentation without target modality ground truth. IEEE Trans. Med. Imaging 38(4), 1016–1025 (2018)CrossRefGoogle Scholar
  15. 15.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)Google Scholar
  16. 16.
    Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)Google Scholar
  17. 17.
    Ketcha, M.D., et al.: Learning-based deformable image registration: effect of statistical mismatch between train and test images. J. Med. Imaging 6(4), 044008 (2019)CrossRefGoogle Scholar
  18. 18.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015)Google Scholar
  19. 19.
    Li, B., et al.: A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 645–653. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-32248-9_72CrossRefGoogle Scholar
  20. 20.
    Li, H., Fan, Y.: Non-rigid image registration using fully convolutional networks with deep self-supervision. arXiv preprint arXiv:1709.00799 (2017)
  21. 21.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  22. 22.
    Mahapatra, D., Antony, B., Sedai, S., Garnavi, R.: Deformable medical image registration using generative adversarial networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 1449–1453. IEEE (2018)Google Scholar
  23. 23.
    Maintz, J.A., Viergever, M.A.: An overview of medical image registration methods. In: Symposium of the Belgian Hospital Physicists Association (SBPH/BVZF), vol. 12, pp. 1–22. Citeseer (1996)Google Scholar
  24. 24.
    Marstal, K., Berendsen, F., Staring, M., Klein, S.: SimpleElastix: a user-friendly, multi-lingual library for medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016)Google Scholar
  25. 25.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
  26. 26.
    Qin, C., et al.: Joint learning of motion estimation and segmentation for cardiac MR image sequences. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 472–480. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00934-2_53CrossRefGoogle Scholar
  27. 27.
    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_19CrossRefGoogle Scholar
  28. 28.
    Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.H.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Imaging 20(6), 526–534 (2001)CrossRefGoogle Scholar
  29. 29.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  30. 30.
    Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)
  31. 31.
    Studholme, C., Hill, D.L., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 32(1), 71–86 (1999)CrossRefGoogle Scholar
  32. 32.
    Sultana, S., Song, D.Y., Lee, J.: A deformable multimodal image registration using PET/CT and TRUS for intraoperative focal prostate brachytherapy. In: Fei, B., Linte, C.A. (eds.) Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10951, pp. 383–388. International Society for Optics and Photonics, SPIE (2019).  https://doi.org/10.1117/12.2512996
  33. 33.
    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 (2018)
  34. 34.
    de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Išgum, I.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128–143 (2019)CrossRefGoogle Scholar
  35. 35.
    de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., Išgum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 204–212. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67558-9_24CrossRefGoogle Scholar
  36. 36.
    Wei, D., et al.: Synthesis and inpainting-based MR-CT registration for image-guided thermal ablation of liver tumors. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 512–520. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-32254-0_57CrossRefGoogle Scholar
  37. 37.
    Woods, R.P.: Handbook of Medical Image Processing and Analysis (2009)Google Scholar
  38. 38.
    Xu, Z., et al.: Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans. Biomed. Eng. 63(8), 1563–1572 (2016)CrossRefGoogle Scholar
  39. 39.
    Xu, Z., Niethammer, M.: DeepAtlas: joint semi-supervised learning of image registration and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 420–429. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-32245-8_47CrossRefGoogle Scholar
  40. 40.
    Yang, X., Akbari, H., Halig, L., Fei, B.: 3D non-rigid registration using surface and local salient features for transrectal ultrasound image-guided prostate biopsy. Proc. SPIE Int. Soc. Opt. Eng. 7964, 79642V–79642V (2011).  https://doi.org/10.1117/12.878153CrossRefGoogle Scholar
  41. 41.
    Zhang, Z., Yang, L., Zheng, Y.: Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)Google Scholar
  42. 42.
    Zheng, H., et al.: Phase collaborative network for multi-phase medical imaging segmentation. ArXiv abs/1811.11814 (2018)Google Scholar
  43. 43.
    Zhou, Y., et al.: Hyper-pairing network for multi-phase pancreatic ductal adenocarcinoma segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 155–163. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-32245-8_18CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.PAII Inc.BethesdaUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA
  3. 3.Vanderbilt UniversityNashvilleUSA
  4. 4.Chang Gung Memorial HospitalTaoyuan CityTaiwan, ROC
  5. 5.Ping An TechnologyShenzhenChina

Personalised recommendations