Abstract
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single ‘4 Chamber Heart’ view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework that enables sensitising atlas generation to disease. In this framework we can jointly learn image segmentation, registration, atlas construction and disease prediction while providing a maximum level of clinical interpretability compared to direct image classification methods. As a result our segmentation allows diagnoses competitive with expert-derived manual diagnosis and yields an AUC-ROC of 0.978 (1043 cases for training, 260 for validation and 325 for testing).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Labelbox (2021). https://labelbox.com
Prevalence charts and tables (2021). https://eu-rd-platform.jrc.ec.europa.eu/eurocat/eurocat-data/prevalence_en
Arnaout, R., et al.: Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning. medRxiv, p. 2020.06.22.20137786 (2020)
Arnaout, R., Curran, L., Chinn, E., Zhao, Y., Moon-Grady, A.: Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions. arXiv (2018)
Baumgartner, C.F., et al.: Sononet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36(11), 2204–2215 (2017)
Budd, S., et al.: Confident head circumference measurement from ultrasound with real-time feedback for sonographers. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 683–691. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_75
Calderon, J., et al.: Impact of prenatal diagnosis on neurocognitive outcomes in children with transposition of the great arteries. J. Pediatr. 161(1), 94–98 (2012)
Carneiro, G., Georgescu, B., Good, S., Comaniciu, D.: Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Trans. Med. Imaging 27(9), 1342–1355 (2008)
Clough, J.R., Oksuz, I., Byrne, N., Schnabel, J.A., King, A.P.: Explicit topological priors for deep-learning based image segmentation using persistent homology. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 16–28. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_2
Holland, B., Myers, J., Woods, C., Jr.: Prenatal diagnosis of critical congenital heart disease reduces risk of death from cardiovascular compromise prior to planned neonatal cardiac surgery: a meta-analysis. Ultrasound Obstet. Gynecol. 45(6), 631–638 (2015)
Hu, X., Li, F., Samaras, D., Chen, C.: Topology-preserving deep image segmentation. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)
Li, J., et al.: Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE J. Biomed. Health Inform. 22(1), 215–223 (2018)
Miceli, F.: A review of the diagnostic accuracy of fetal cardiac anomalies. Australasian J. Ultrasound Med. 18(1), 3–9 (2015)
Monteiro, M., et al.: Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 12756–12767. Curran Associates, Inc. (2020)
NHS: NHS Fetal Anomaly Screening Programme Handbook Valid from August 2018. Technical report (2018)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Rasmussen, C., Nickisch, H.: Gaussian processes for machine learning (gpml) toolbox. J. Mach. Learn. Res. 11, 3011–3015 (2010)
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_28
Rueda, S., et al.: Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans. Med. Imaging 33(4), 797–813 (2014)
Sinclair, M., et al.: Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 714–717. IEEE (2018)
Sinclair, M., et al.: Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks. arXiv (2020)
Sushma, T.V., Sriraam, N., Megha Arakeri, P., Suresh, S.: Classification of fetal heart ultrasound images for the detection of CHD. In: Raj, J.S., Iliyasu, A.M., Bestak, R., Baig, Z.A. (eds.) Innovative Data Communication Technologies and Application. LNDECT, vol. 59, pp. 489–505. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-9651-3_41
Tan, J., et al.: Automated detection of congenital heart disease in fetal ultrasound screening. In: Hu, Y., et al. (eds.) ASMUS/PIPPI -2020. LNCS, vol. 12437, pp. 243–252. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60334-2_24
Wu, L., et al.: Cascaded fully convolutional networks for automatic prenatal ultrasound image segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 663–666. IEEE (2017)
Yeo, L., Romero, R.: Fetal Intelligent Navigation Echocardiography (FINE): a novel method for rapid, simple, and automatic examination of the fetal heart. Ultrasound Obstet. Gynecol. 42(3), 268–284 (2013)
Acknowledgements
We thank the volunteers and sonographers at St. Thomas’ Hospital London. The work of E.C.R. was supported by the Academy of Medical Sciences/the British Heart Foundation/the Government Department of Business, Energy and Industrial Strategy/the Wellcome Trust Springboard Award [SBF003/1116]. We also gratefully acknowledge financial support from the Wellcome Trust IEH 102431, EPSRC (EP/S022104/1, EP/S013687/1), EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z], the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and supported by the NIHR Clinical Research Facility (CRF) at Guy’s and St Thomas’, and Nvidia GPU donations.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Budd, S. et al. (2021). Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-Specific Atlas Maps. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-87234-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87233-5
Online ISBN: 978-3-030-87234-2
eBook Packages: Computer ScienceComputer Science (R0)