Abstract
Fetal brain MRI is useful for diagnosing brain abnormalities but is challenged by fetal motion. The current protocol for T2-weighted fetal brain MRI is not robust to motion so image volumes are degraded by inter- and intra- slice motion artifacts. Besides, manual annotation for fetal MR image quality assessment are usually time-consuming. Therefore, in this work, a semi-supervised deep learning method that detects slices with artifacts during the brain volume scan is proposed. Our method is based on the mean teacher model, where we not only enforce consistency between student and teacher models on the whole image, but also adopt an ROI consistency loss to guide the network to focus on the brain region. The proposed method is evaluated on a fetal brain MR dataset with 11,223 labeled images and more than 200,000 unlabeled images. Results show that compared with supervised learning, the proposed method can improve model accuracy by about 6% and outperform other state-of-the-art semi-supervised learning methods. The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Esses, S.J., et al.: Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture. J. Magn. Reson. Imaging 47(3), 723–728 (2018)
Gholipour, A., et al.: Fetal MRI: a technical update with educational aspirations. Concepts Magn. Reson. Part A 43(6), 237–266 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Label propagation for deep semi-supervised learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5070–5079 (2019)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kline-Fath, B., Bahado-Singh, R., Bulas, D.: Fundamental and Advanced Fetalimaging: Ultrasound and MRI. Lippincott Williams & Wilkins, Philadelphia (2014)
Kul, S., et al.: Contribution of MRI to ultrasound in the diagnosis of fetal anomalies. J. Magn. Reson. Imaging 35(4), 882–890 (2012)
Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)
Malamateniou, C., et al.: Motion-compensation techniques in neonatal and fetal MR imaging. Am. J. Neuroradiol. 34(6), 1124–1136 (2013)
Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)
Salehi, S.S.M., et al.: Real-time automatic fetal brain extraction in fetal MRI by deep learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 720–724. IEEE (2018)
Schreiber-Zinaman, J., Rosenkrantz, A.B.: Frequency and reasons for extra sequences in clinical abdominal MRI examinations. Abdom. Radiol. 42(1), 306–311 (2017)
Shang, H., et al.: Leveraging other datasets for medical imaging classification: evaluation of transfer, multi-task and semi-supervised learning. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 431–439. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32254-0_48
Su, H., Shi, X., Cai, J., Yang, L.: Local and global consistency regularized mean teacher for semi-supervised nuclei classification. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 559–567. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_62
Sujit, S.J., Coronado, I., Kamali, A., Narayana, P.A., Gabr, R.E.: Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks. J. Magn. Reson. Imaging 50, 1260–1267 (2019)
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, pp. 1195–1204 (2017)
Tourbier, S., Bresson, X., Hagmann, P., Thiran, J.P., Meuli, R., Cuadra, M.B.: An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization. NeuroImage 118, 584–597 (2015)
Xu, J., et al.: Fetal pose estimation in volumetric MRI using a 3D convolution neural network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 403–410. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_44
Yarowsky, D.: Unsupervised word sense disambiguation rivaling supervised methods. In: 33rd Annual Meeting of the Association for Computational Linguistics, pp. 189–196 (1995)
Zaitsev, M., Maclaren, J., Herbst, M.: Motion artifacts in MRI: a complex problem with many partial solutions. J. Magn. Reson. Imaging 42(4), 887–901 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, J. et al. (2020). Semi-supervised Learning for Fetal Brain MRI Quality Assessment with ROI Consistency. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-59725-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59724-5
Online ISBN: 978-3-030-59725-2
eBook Packages: Computer ScienceComputer Science (R0)
