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Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-Propagation

  • Shiqi Peng
  • Bolin Lai
  • Guangyu Yao
  • Xiaoyun Zhang
  • Ya ZhangEmail author
  • Yan-Feng Wang
  • Hui Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11795)

Abstract

Vertebral body (VB) segmentation is an important preliminary step towards medical visual diagnosis for spinal diseases. However, most previous works require pixel/voxel-wise strong supervisions, which is expensive, tedious and time-consuming for experts to annotate. In this paper, we propose a Weakly supervised Iterative Spinal Segmentation (WISS) method leveraging only four corner landmark weak labels on a single sagittal slice to achieve automatic volumetric segmentation from CT images for VBs. WISS first segments VBs on an annotated sagittal slice in an iterative self-training manner. This self-training method alternates between training and refining labels in the training set. Then WISS proceeds to segment the whole VBs slice by slice with a slice-propagation method to obtain volumetric segmentations. We evaluate the performance of WISS on a private spinal metastases CT dataset and the public lumbar CT dataset. On the first dataset, WISS achieves distinct improvements with regard to two different backbones. For the second dataset, WISS achieves dice coefficients of \(91.7\%\) and \(83.7\%\) for mid-sagittal slices and 3D CT volumes, respectively, saving a lot of labeling costs and only sacrificing a little segmentation performance.

Keywords

Vertebral body segmentation Weak supervision 

References

  1. 1.
    Ali, A.M., Aslan, M.S., Farag, A.A.: Vertebral body segmentation with prior shape constraints for accurate BMD measurements. Comput. Med. Imaging Graph. 38(7), 586–595 (2014)CrossRefGoogle Scholar
  2. 2.
    Cai, J., et al.: Accurate weakly-supervised deep lesion segmentation using large-scale clinical annotations: slice-propagated 3D mask generation from 2D RECIST. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 396–404. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00937-3_46CrossRefGoogle Scholar
  3. 3.
    Chu, C., Belavỳ, D.L., Armbrecht, G., Bansmann, M., Felsenberg, D., Zheng, G.: Fully automatic localization and segmentation of 3d vertebral bodies from CT/MR images via a learning-based method. PLoS ONE 10(11), e0143327 (2015)CrossRefGoogle Scholar
  4. 4.
    Gutman, D., et al.: Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1605.01397 (2016)
  5. 5.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)Google Scholar
  6. 6.
    Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T.: Shape representation for efficient landmark-based segmentation in 3-D. IEEE Trans. Med. Imaging 33(4), 861–874 (2014)CrossRefGoogle Scholar
  7. 7.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Advances in Neural Information Processing Systems, pp. 109–117 (2011)Google Scholar
  8. 8.
    Lee, H.W., Kim, N.R., Lee, J.H.: Deep neural network self-training based on unsupervised learning and dropout. Int. J. Fuzzy Logic Intell. Syst. 17(1), 1–9 (2017)CrossRefGoogle Scholar
  9. 9.
    Lessmann, N., van Ginneken, B., de Jong, P.A., Išgum, I.: Iterative fully convolutional neural networks for automatic vertebra segmentation. arXiv preprint arXiv:1804.04383 (2018)
  10. 10.
    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).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
  11. 11.
    Sarker, M.M.K., et al.: SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 21–29. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00934-2_3CrossRefGoogle Scholar
  12. 12.
    Štern, D., Likar, B., Pernuš, F., Vrtovec, T.: Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Phys. Med. Biol. 56(23), 7505 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shiqi Peng
    • 1
  • Bolin Lai
    • 1
  • Guangyu Yao
    • 2
  • Xiaoyun Zhang
    • 1
  • Ya Zhang
    • 1
    Email author
  • Yan-Feng Wang
    • 1
  • Hui Zhao
    • 2
  1. 1.Cooperative Medianet Innovation CenterShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiPeople’s Republic of China

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