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Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning

  • Miaoyun Zhao
  • Li Wang
  • Jiawei Chen
  • Dong Nie
  • Yulai Cong
  • Sahar Ahmad
  • Angela Ho
  • Peng Yuan
  • Steve H. Fung
  • Hannah H. Deng
  • James XiaEmail author
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11073)

Abstract

Automatic segmentation of medical images finds abundant applications in clinical studies. Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning of craniomaxillofacial (CMF) surgeries as it shows clear bony structures. However, CT imaging poses radiation risks for the subjects being scanned. Alternatively, Magnetic Resonance Imaging (MRI) is considered to be safe and provides good visualization of the soft tissues, but the bony structures appear invisible from MRI. Therefore, the segmentation of bony structures from MRI is quite challenging. In this paper, we propose a cascaded generative adversarial network with deep-supervision discriminator (Deep-supGAN) for automatic bony structures segmentation. The first block in this architecture is used to generate a high-quality CT image from an MRI, and the second block is used to segment bony structures from MRI and the generated CT image. Different from traditional discriminators, the deep-supervision discriminator distinguishes the generated CT from the ground-truth at different levels of feature maps. For segmentation, the loss is not only concentrated on the voxel level but also on the higher abstract perceptual levels. Experimental results show that the proposed method generates CT images with clearer structural details and also segments the bony structures more accurately compared with the state-of-the-art methods.

References

  1. 1.
    Brenner, D.J., Hall, E.J.: Computed tomography-an increasing source of radiation exposure. N. Engl. J. Med. 357(22), 2277–2284 (2007)CrossRefGoogle Scholar
  2. 2.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on CVPR, pp. 3431–3440 (2015)Google Scholar
  3. 3.
    Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, Mert R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46723-8_49CrossRefGoogle Scholar
  4. 4.
    Nie, D., et al.: Segmentation of craniomaxillofacial bony structures from MRI with a 3D deep-learning based cascade framework. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 266–273. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67389-9_31CrossRefGoogle Scholar
  5. 5.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  6. 6.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  7. 7.
    Trzepacz, P.T., Yu, P., Sun, J., et al.: Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer’s dementia. Neurobiol. Aging 35(1), 143–151 (2014)CrossRefGoogle Scholar
  8. 8.
    Luc, P., Couprie, C., Chintala, S.: Semantic segmentation using adversarial networks. arXiv preprint arXiv:1611.08408 (2016)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Miaoyun Zhao
    • 1
  • Li Wang
    • 1
  • Jiawei Chen
    • 1
  • Dong Nie
    • 1
  • Yulai Cong
    • 2
  • Sahar Ahmad
    • 1
  • Angela Ho
    • 3
  • Peng Yuan
    • 3
  • Steve H. Fung
    • 3
  • Hannah H. Deng
    • 3
  • James Xia
    • 3
    Email author
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Electrical and Computer EngineeringDuke UniversityDurhamUSA
  3. 3.Houston Methodist HospitalHoustonUSA

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