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Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images

  • Veronika A. ZimmerEmail author
  • Alberto Gomez
  • Emily Skelton
  • Nicolas Toussaint
  • Tong Zhang
  • Bishesh Khanal
  • Robert Wright
  • Yohan Noh
  • Alison Ho
  • Jacqueline Matthew
  • Joseph V. Hajnal
  • Julia A. Schnabel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11768)

Abstract

We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.

Notes

Acknowledgements

This work was supported by the Wellcome Trust IEH Award [102431], by the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z] and by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

References

  1. 1.
    Torrents-Barrena, J., et al.: Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med. Imag. Anal. 51, 61–88 (2019)CrossRefGoogle Scholar
  2. 2.
    Miao, H., et al.: Placenta maps: in utero placental health assessment of the human fetus. IEEE Trans. Vis. Comp. Graph. 23(6), 1612–1623 (2017)CrossRefGoogle Scholar
  3. 3.
    Torrents-Barrena, J., et al.: Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med. Imag. Anal. 54, 263–279 (2019)CrossRefGoogle Scholar
  4. 4.
    Stevenson, G.N., Collins, S.L., Ding, J., Impey, L., Noble, J.A.: 3-D ultrasound segmentation of the placenta using the random walker algorithm: reliability and agreement. Ultras. Med. Biol. 41(12), 3182–3193 (2015)CrossRefGoogle Scholar
  5. 5.
    Oguz, B.U., et al.: Combining deep learning and multi-atlas label fusion for automated placenta segmentation from 3DUS. In: Melbourne, A., et al. (eds.) PIPPI/DATRA -2018. LNCS, vol. 11076, pp. 138–148. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00807-9_14CrossRefGoogle Scholar
  6. 6.
    Looney, P., et al.: Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning. JCI Insight 3(11), e120178 (2018).  https://doi.org/10.1172/jci.insight.120178
  7. 7.
    Yang, X., et al.: Towards automated semantic segmentation in prenatal volumetric ultrasound. IEEE Trans. Med. Imag. 38(1), 180–193 (2018)CrossRefGoogle Scholar
  8. 8.
    Wachinger, C., Wein, W., Navab, N.: Three-dimensional ultrasound mosaicing. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007. LNCS, vol. 4792, pp. 327–335. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-75759-7_40 CrossRefGoogle Scholar
  9. 9.
    Ni, D., et al.: Volumetric ultrasound panorama based on 3D SIFT. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 52–60. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85990-1_7CrossRefGoogle Scholar
  10. 10.
    Gomez, A., Bhatia, K., Tharin, S., Housden, J., Toussaint, N., Schnabel, J.A.: Fast registration of 3D fetal ultrasound images using learned corresponding salient points. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA -2017. LNCS, vol. 10554, pp. 33–41. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67561-9_4CrossRefGoogle Scholar
  11. 11.
    Zimmer, V.A., et al.: Multi-view image reconstruction: application to fetal ultrasound compounding. In: Melbourne, A., et al. (eds.) PIPPI/DATRA -2018. LNCS, vol. 11076, pp. 107–116. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00807-9_11CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Imag. Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  14. 14.
    Gomez, A., et al.: Regional differences in end-diastolic volumes between 3D echo and CMR in HLHS patients. Front. Pediatr. 4, 133 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Veronika A. Zimmer
    • 1
    Email author
  • Alberto Gomez
    • 1
  • Emily Skelton
    • 1
  • Nicolas Toussaint
    • 1
  • Tong Zhang
    • 1
  • Bishesh Khanal
    • 1
    • 2
  • Robert Wright
    • 1
  • Yohan Noh
    • 1
    • 3
  • Alison Ho
    • 4
  • Jacqueline Matthew
    • 1
  • Joseph V. Hajnal
    • 1
  • Julia A. Schnabel
    • 1
  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  2. 2.Nepal Applied Mathematics and Informatics Institute for Research (NAAMII)KathmanduNepal
  3. 3.Department of Mechanical and Aerospace EngineeringBrunel University LondonUxbridgeUK
  4. 4.Department of Women and Children’s Health, School of Life Course SciencesKing’s College LondonLondonUK

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