Skip to main content

Automatic Fetal Gestational Age Estimation from First Trimester Scans

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12967)


Automatic Gestational Age (GA) estimation based on the Crown Rump Length (CRL) measurement is the preferred solution to overcome the challenges while using the last menstrual period (LMP) to date pregnancies. However, GA estimation based on CRL requires accurate placement of calipers on the fetal crown and rump which is not always a straightforward task, especially for an inexperienced sonographer. This paper proposes an accurate GA estimation method from fetal CRL images during the first trimester scan. The method addresses this problem by segmenting the fetus using a binary and multi-class U-Net. The fetal segmentation is used to compute the CRL. This is then followed by an estimation of GA from the automatic CRL measurement based of clinical information. The results from the multi-class segmentation achieves a more accurate precision, recall, Dice, and Jaccard. This has also led to a more accurate CRL measurement and hence more robust GA estimation.


  • Fetal ultrasound
  • Deep learning
  • Crown-rump length
  • Gestational age estimation
  • Fetal growth

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

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-87583-1_22
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-87583-1
  • 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.
Fig. 3.


  1. Zupan, J.: Perinatal mortality in developing countries. N. Engl. J. Med. 352, 2047–2048 (2005)

    CrossRef  Google Scholar 

  2. Karl, S., et al.: Preterm or not-an evaluation of estimates of gestational age in a cohort of women from Rural Papua New Guinea. PLoS ONE 10, e0124286 (2015)

    Google Scholar 

  3. Rijken, M.J., et al.: Quantifying low birth weight, preterm birth and small-for-gestational-age effects of malaria in pregnancy: a population cohort study. PLoS ONE 9, e100247 (2014)

    Google Scholar 

  4. Alexander, G.R., Tompkins, M.E., Petersen, D.J., Hulsey, T.C., Mor, J.: Discordance between LMP-based and clinically estimated gestational age: implications for research, programs, and policy. Public Health Rep. 110, 395–402 (1995)

    Google Scholar 

  5. Callaghan, W.M., Dietz, P.M.: Differences in birth weight for gestational age distributions according to the measures used to assign gestational age. Am. J. Epidemiol. 171, 826–836 (2010)

    CrossRef  Google Scholar 

  6. Whitworth, M., Bricker, L., Mullan, C.: Ultrasound for fetal assessment in early pregnancy. Cochrane Database Syst. Rev. CD007058 (2015)

    Google Scholar 

  7. Papageorghiou, A.T., et al.: International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown-rump length in the first trimester of pregnancy. Fetal International, and Century Newborn Growth Consortium for the 21st. Ultrasound Obstet. Gynecol. 44, 641–648 (2014)

    Google Scholar 

  8. Bradburn, E.H., Hin Lee, L., Noble, J.A., Papageorghiou, A.T.: OC10.04: estimating fetal gestational age based on ultrasound image characteristics using artificial intelligence. Ultrasound Obstetr. Gynecol. 56, 28–29 (2020)

    Google Scholar 

  9. Bradburn, E., Mohammad, Y., Noble, J., Papageorghiou, A.: OC10.05: an artificial intelligence system that can correctly identify fetal ultrasound imaging planes throughout gestational age. Ultrasound Obstet. Gynecol. 56, 29 (2020).

  10. Włodarczyk, T., et al.: Estimation of preterm birth markers with U-Net segmentation network. In: Wang, Q., et al. (eds.) PIPPI/SUSI-2019. LNCS, vol. 11798, pp. 95–103. Springer, Cham (2019).

    CrossRef  Google Scholar 

  11. Włodarczyk, T., et al.: Spontaneous preterm birth prediction using convolutional neural networks. In: Hu, Y., et al. (eds.) ASMUS/PIPPI -2020. LNCS, vol. 12437, pp. 274–283. Springer, Cham (2020).

    CrossRef  Google Scholar 

  12. Namburete, A.I.L., Xie, W., Noble, J.A.: Robust regression of brain maturation from 3D fetal neurosonography using CRNs. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA-2017. LNCS, vol. 10554, pp. 73–80. Springer, Cham (2017).

    CrossRef  Google Scholar 

  13. 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).

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Sevim Cengiz .

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

Cengiz, S., Yaqub, M. (2021). Automatic Fetal Gestational Age Estimation from First Trimester Scans. In: Noble, J.A., Aylward, S., Grimwood, A., Min, Z., Lee, SL., Hu, Y. (eds) Simplifying Medical Ultrasound. ASMUS 2021. Lecture Notes in Computer Science(), vol 12967. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87582-4

  • Online ISBN: 978-3-030-87583-1

  • eBook Packages: Computer ScienceComputer Science (R0)