Skip to main content

Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture

  • 973 Accesses

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12959)

Abstract

Diseases related to the placenta, such as preeclampsia (PE) and fetal growth restriction (FGR), are major causes of mortality and morbidity. Diagnostic criteria of such diseases are defined by biomarkers, such as proteinuria, that appear in advanced gestational age. As placentally-mediated disease is often clinically unrecognized until later stages, accurate early diagnosis is required to allow earlier intervention, which is particularly challenging in low-resource areas without subspecialty clinicians. Proposed attempts at early diagnosis involve a combination of subjective and objective ultrasound placental assessments which have limited accuracy and high interobserver variability. Machine learning, particularly with convolutional neural networks, have shown potential in analyzing complex textural features in ultrasound imaging that may be predictive of disease. We propose a model utilizing a two-stage convolutional neural network pipeline to classify the presence of placental disease. The pipeline involves a segmentation stage to extract the placenta followed by a classification stage. We evaluated the pipeline on retrospectively collected placenta ultrasound scans and diagnostic outcomes of 321 patients taken by 18 sonographers and 3 ultrasound machines. Compared to existing clinical algorithms and neural networks, our classifier achieved significantly higher accuracy of 0.81 ± 0.02 (p < 0.05). Class activation maps were generated to identify potential abnormal regions of interest in placenta tissue. This study provides support that automated image analysis of ultrasound texture may assist physicians in early identification of placental disease, with potential benefits to low-resource environments.

Keywords

  • Placenta
  • Ultrasound
  • Convolutional neural networks
  • Classification
  • Preeclampsia
  • Fetal growth restriction

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-87735-4_14
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-87735-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

References

  1. Mackay, A., Berge, C., Atrash, H.: Pregnancy-related mortality from preeclampsia and eclampsia. Obstet. Gynecol. 97(4), 533–538 (2001)

    Google Scholar 

  2. Garite, T.J., Clark, R., Thorpe, J.A.: Intrauterine growth restriction increases morbidity and mortality among premature neonates. Am. J. Obstet. Gynecol. 191(2), 481–487 (2004)

    CrossRef  Google Scholar 

  3. Redman, C.W.: Latest advances in understanding preeclampsia. Science 308(5728), 1592–1594 (2005)

    CrossRef  Google Scholar 

  4. Leslie, K., Thilaganathan, B., Papageorghiou, A.: Early prediction and prevention of pre-eclampsia. Best Pract. Res. Clin. Obstet. Gynaecol. 25(3), 343–354 (2011)

    CrossRef  Google Scholar 

  5. Mol, B.W., Roberts, C.T., Thangarantinam, S., Magee, L.A., De Groot, C.J., Hofmeyr, G.J.: Pre-eclampsia. Lancet 387(10022), 999–1011 (2016)

    CrossRef  Google Scholar 

  6. Rolnik, D.L., Wright, D., Poon, L.C., O’Gorman, N., Syngelaki, A., de Paco Matallana, C., Akolekar, R., Cicero, S., Janga, D., Singh, M., Molina, F.S.: Aspirin versus placebo in pregnancies at high risk for preterm preeclampsia. N. Engl. J. Med. 377(7), 613–622 (2017)

    CrossRef  Google Scholar 

  7. Romero, R.: Prenatal medicine: the child is the father of the man. J. Matern. Neonatal Med. 22(8), 636–639 (2009)

    CrossRef  Google Scholar 

  8. A. C. of Obstetricians and Gynecologists: CO638: first-trimester risk assessment for early-onset pre-eclampsia. Obstet. Gynecol. 126(638), 25–274 (2015)

    Google Scholar 

  9. Deeba, F., et al.: Multiparametric QUS analysis for placental tissue characterization. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3477–3480 (2018)

    Google Scholar 

  10. O’Gorman, N., Nicolaides, K.H., Poon, L.C.Y.: The use of ultrasound and other markers for early detection of preeclampsia: Women’s Health, pp. 197–207 (2012)

    Google Scholar 

  11. Moreira, M.W.L., Rorigues, J.J.P.C., Oliveira, A.M.B., Ramos, R.F., Saleem, K.: A preeclampsia diagnosis approach using Bayesian networks. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–5 (2016)

    Google Scholar 

  12. Jhee, J., et al.: Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS ONE 14(8), e0221202 (2019)

    CrossRef  Google Scholar 

  13. Sufriyana, H., Wu, Y., Su, E.: Prediction of preeclampsia and intrauterine growth restriction: development of machine learning models on a prospective cohort. JMIR Med. Inform. 8(5), 215411 (2020)

    CrossRef  Google Scholar 

  14. Qi, H., Collins, S., Noble, J.A.: Automatic lacunae localization in placental ultrasound images via layer aggregation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 921–929. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_102

    CrossRef  Google Scholar 

  15. Hu, R., Singla, R., Yan, R., Mayer, C., Rohling R.N.: Automated placenta segmentation with a convolutional neural network weighted by acoustic shadow detection. In: Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6718–6723 (2019)

    Google Scholar 

  16. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017, pp. 4700–4708 (2017)

    Google Scholar 

  17. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 770–778 (2016)

    Google Scholar 

  18. Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  19. Tan, M., Le, Quoc.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114 (2019)

    Google Scholar 

  20. Yang, W., Huang, H., Zhang, Z., Chen, X., Huang, K., Zhang, S.: Towards rich feature discovery with class activation maps augmentation for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019, pp. 1389–1398 (2019)

    Google Scholar 

  21. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. Image Signal Process. 42(11), 1–13 (2018)

    Google Scholar 

  22. Castro, D.C., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11(1), 1–10 (2020)

    CrossRef  Google Scholar 

Download references

Acknowledgments

Funding for this study was provided by The Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes of Health Research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zoe Hu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Hu, Z., Hu, R., Yan, R., Mayer, C., Rohling, R.N., Singla, R. (2021). Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture. In: , et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham. https://doi.org/10.1007/978-3-030-87735-4_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87735-4_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87734-7

  • Online ISBN: 978-3-030-87735-4

  • eBook Packages: Computer ScienceComputer Science (R0)