Abstract
The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effective semi-supervised learning method for improving placenta segmentation in fetal EPI MRI time series. We introduce consistency regularization loss that promotes consistency under spatial transformation of the same image and temporal consistency across nearby images in a time series. The experimental results show that the method improves the overall segmentation accuracy and provides better performance for outliers and hard samples. The evaluation also indicates that our method improves the temporal coherency of the prediction, which could lead to more accurate computation of temporal placental biomarkers. This work contributes to the study of the placenta and prenatal clinical decision-making.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abulnaga, S.M., Abaci Turk, E., Bessmeltsev, M., Grant, P.E., Solomon, J., Golland, P.: Placental flattening via volumetric parameterization. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 39–47. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_5
Abulnaga, S.M., et al.: Automatic segmentation of the placenta in BOLD MRI time series. In: Licandro, R., Melbourne, A., Abaci Turk, E., Macgowan, C., Hutter, J. (eds.) International Workshop on Preterm, Perinatal and Paediatric Image Analysis, vol. 13575, pp. 1–12. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17117-8_1
Alansary, A., et al.: Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 589–597. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_68
Baur, C., Albarqouni, S., Navab, N.: Semi-supervised deep learning for fully convolutional networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) Medical Image Computing and Computer Assisted Intervention - MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, 11–13 September 2017, Proceedings, Part III 20, vol. 10435, pp. 311–319. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_36
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional Siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) Computer Vision-ECCV 2016 Workshops: Amsterdam, The Netherlands, 8–10 October and 15–16 October 2016, Proceedings, Part II 14, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56
Chen, X., He, K.: Exploring simple Siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
Chicco, D.: Siamese neural networks: an overview. In: Artificial Neural Networks, pp. 73–94 (2021)
Cui, W., et al.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds.) Information Processing in Medical Imaging: 26th International Conference, IPMI 2019, Hong Kong, China, 2–7 June 2019, Proceedings 26, vol. 11492, pp. 554–565. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_43
Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, 25–30 June 2017, Proceedings 25, vol. 10265, pp. 597–609. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_47
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
León, R.L., Li, K.T., Brown, B.P.: A retrospective segmentation analysis of placental volume by magnetic resonance imaging from first trimester to term gestation. Pediatr. Radiol. 48(13), 1936–1944 (2018)
Li, X., Yu, L., Chen, H., Fu, C.W., Xing, L., Heng, P.A.: Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans. Neural Networks Learn. Syst. 32(2), 523–534 (2020)
Ren, M., Dey, N., Styner, M., Botteron, K., Gerig, G.: Local spatiotemporal representation learning for longitudinally-consistent neuroimage analysis. Adv. Neural. Inf. Process. Syst. 35, 13541–13556 (2022)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sokloska, M., et al.: Placental image analysis using coupled diffusion-weighted and multi-echo T2 MRI and a multi-compartment model. In: MICCAI Workshop on Perinatal, Preterm and Paediatric Image Analysis (PIPPI) (2016)
Taleb, A., et al.: 3D self-supervised methods for medical imaging. Adv. Neural. Inf. Process. Syst. 33, 18158–18172 (2020)
Tang, Y., et al.: Self-supervised pre-training of Swin transformers for 3D medical image analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20730–20740 (2022)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Xia, J., He, Y., Yin, X., Han, S., Gu, X.: Direct-product volumetric parameterization of handlebodies via harmonic fields. In: Shape Modeling International Conference, pp. 3–12. IEEE (2010)
Xu, J., et al.: Semi-supervised learning for fetal brain MRI quality assessment with ROI consistency. In: Martel, A.L., et al. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, 4–8 October 2020, Proceedings, Part VI 23, vol. 12266, pp. 386–395. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_37
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196 (1995)
Acknowledgements
This research is supported by NIH NIBIB NAC P41EB015902, NIH NICHD R01HD100009, and NIH NIBIB 5R01EB032708, and the Swiss National Science Foundation project P500PT-206955.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Y. et al. (2023). Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2023. Lecture Notes in Computer Science, vol 14246. Springer, Cham. https://doi.org/10.1007/978-3-031-45544-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-45544-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45543-8
Online ISBN: 978-3-031-45544-5
eBook Packages: Computer ScienceComputer Science (R0)