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Deep Image-to-Image Recurrent Network with Shape Basis Learning for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes

  • Dong Yang
  • Tao Xiong
  • Daguang Xu
  • S. Kevin Zhou
  • Zhoubing Xu
  • Mingqing Chen
  • JinHyeong Park
  • Sasa Grbic
  • Trac D. Tran
  • Sang Peter Chin
  • Dimitris Metaxas
  • Dorin Comaniciu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Automatic vertebra localization and identification in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postoperative assessment. In this paper, we propose an automatic and efficient algorithm to localize and label the vertebra centroids in 3D CT volumes. First, a deep image-to-image network (DI2IN) is deployed to initialize vertebra locations, employing the convolutional encoder-decoder architecture. Next, the centroid probability maps from DI2IN are modeled as a sequence according to the spatial relationship of vertebrae, and evolved with the convolutional long short-term memory (ConvLSTM) model. Finally, the landmark positions are further refined and regularized by another neural network with a learned shape basis. The whole pipeline can be conducted in the end-to-end manner. The proposed method outperforms other state-of-the-art methods on a public database of 302 spine CT volumes with various pathologies. To further boost the performance and validate that large labeled training data can benefit the deep learning algorithms, we leverage the knowledge of additional 1000 3D CT volumes from different patients. Our experimental results show that training with a large database improves the performance of proposed framework by a large margin and achieves an identification rate of 89%.

Notes

Acknowledgements

We thank Dr. David Liu who provided insight and expertise that greatly assisted the research.

References

  1. 1.
    Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33454-2_73 CrossRefGoogle Scholar
  2. 2.
    Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40763-5_33 CrossRefGoogle Scholar
  3. 3.
    Chen, H., Shen, C., Qin, J., Ni, D., Shi, L., Cheng, J.C.Y., Heng, P.-A.: Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 515–522. Springer, Cham (2015). doi: 10.1007/978-3-319-24553-9_63 CrossRefGoogle Scholar
  4. 4.
    Suzani, A., Seitel, A., Liu, Y., Fels, S., Rohling, R.N., Abolmaesumi, P.: Fast automatic vertebrae detection and localization in pathological CT scans - a deep learning approach. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 678–686. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_81 CrossRefGoogle Scholar
  5. 5.
    Payer, C., Štern, D., Bischof, H., Urschler, M.: Regressing heatmaps for multiple landmark localization using CNNs. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 230–238. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_27 CrossRefGoogle Scholar
  6. 6.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)
  7. 7.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  8. 8.
    Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)Google Scholar
  9. 9.
    Merkow, J., Kriegman, D., Marsden, A., Tu, Z.: Dense volume-to-volume vascular boundary detection. arXiv preprint arXiv:1605.08401 (2016)
  10. 10.
    Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J., Heng, P.-A.: 3D deeply supervised network for automatic liver segmentation from CT volumes. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 149–157. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_18 CrossRefGoogle Scholar
  11. 11.
    Yu, X., Zhou, F., Chandraker, M.: Deep deformation network for object landmark localization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 52–70. Springer, Cham (2016). doi: 10.1007/978-3-319-46454-1_4 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dong Yang
    • 1
  • Tao Xiong
    • 2
  • Daguang Xu
    • 3
  • S. Kevin Zhou
    • 3
  • Zhoubing Xu
    • 3
  • Mingqing Chen
    • 3
  • JinHyeong Park
    • 3
  • Sasa Grbic
    • 3
  • Trac D. Tran
    • 2
  • Sang Peter Chin
    • 2
  • Dimitris Metaxas
    • 1
  • Dorin Comaniciu
    • 3
  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  2. 2.Department of Electrical and Computer EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  3. 3.Medical Imaging Technologies, Siemens Healthcare Technology CenterPrincetonUSA

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