Abstract
With the rise of deep learning applications to medical imaging, there has been a growing appetite for large and well-annotated datasets, yet annotation is time-consuming and hard to come by. In this work, we train a 3D semantic segmentation model in an advanced semi-supervised learning fashion. The proposed SSL framework consists of three models: a Student model that learns from annotated data and a large amount of raw data, a Teacher model with the same architecture as the student, updated by self-ensembling and which supervises the student through pseudo-labels, and an Examiner model that assesses the quality of the student’s inferences. All three models are built with 3D convolutional operations. The overall framework mimics a collaboration between a consistency training Student \(\leftrightarrow \) Teacher module and an adversarial training Examiner \(\leftrightarrow \) Student module. The proposed method is validated with various evaluation metrics on a public benchmarking 3D MRI brain tumor segmentation dataset. The experimental results of the proposed method outperform pre-existing semi-supervised methods. The source code, baseline methods, and dataset are available at https://github.com/ziyangwang007/CV-SSL-MIS.
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
Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE TMI 37(11), 2514–2525 (2018)
Chen, L.-C., et al.: Naive-student: leveraging semi-supervised learning in video sequences for urban scene segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 695–714. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_40
Chen, X., et al.: Semi-supervised semantic segmentation with cross pseudo supervision. In: CVPR (2021)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cui, W., et al.: Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: IPMI (2019)
David, B., et al.: Mixmatch: a holistic approach to semi-supervised learning. In: NeurIPS (2019)
Dong-DongChen, et al.: Tri-net for semi-supervised deep learning. In: IJCAI (2018)
Fang, K., Li, W.-J.: DMNet: difference minimization network for semi-supervised segmentation in medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 532–541. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_52
French, G., et al.: Semi-supervised semantic segmentation needs strong, varied perturbations. In: BMVC (2019)
Hung, W.C., et al.: Adversarial learning for semi-supervised semantic segmentation. In: BMVC (2018)
Ke, Z., et al.: Dual student: breaking the limits of the teacher in semi-supervised learning. In: CVPR (2019)
Kihyuk, S., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. In: NeurIPS (2020)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BraTS). IEEE TMI 34(10), 1993–2024 (2014)
Mittal, S., Tatarchenko, M., Brox, T.: Semi-supervised semantic segmentation with high-and low-level consistency. IEEE TPAMI 43(4), 1369–1379 (2019)
Nasim, S., et al.: Semi supervised semantic segmentation using generative adversarial network. In: ICCV (2017)
Ouali, Y., et al.: Semi-supervised semantic segmentation with cross-consistency training. In: CVPR (2020)
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
Samuli, L., Aila, T.: Temporal ensembling for semi-supervised learning. In: ICLR (2016)
Shanis, Z., Gerber, S., Gao, M., Enquobahrie, A.: Intramodality domain adaptation using self ensembling and adversarial training. In: Wang, Q., et al. (eds.) DART/MIL3ID-2019. LNCS, vol. 11795, pp. 28–36. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33391-1_4
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Takeru, M., et al.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE TPAMI 41(8), 1979–1993 (2018)
Tarvainen, A., et al.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NeurIPS (2017)
Verma, V., et al.: Interpolation consistency training for semi-supervised learning. In: IJCAI (2019)
Vu, T.H., et al.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: CVPR (2019)
Wang, K., et al.: Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning. MedIA (2022)
Wang, Z., et al.: RAR-U-net: a residual encoder to attention decoder by residual connections framework for spine segmentation under noisy labels. In: ICIP. IEEE (2021)
Wang, Z., et al.: Triple-view feature learning for medical image segmentation. In: MICCAI-W (2022)
Wang, Z., et al.: Uncertainty-aware transformer for MRI cardiac segmentation via mean teachers. In: MIUA (2022)
Wang, Z., Dong, N., Voiculescu, I.: Computationally-efficient vision transformer for medical image semantic segmentation via dual pseudo-label supervision. In: ICIP. IEEE (2022)
Wang, Z., Voiculescu, I.: Dealing with unreliable annotations: a noise-robust network for semantic segmentation through a transformer-improved encoder and convolution decoder. Appl. Sci. 13(13), 7966 (2023)
Yeghiazaryan, V., et al.: Family of boundary overlap metrics for the evaluation of medical image segmentation. SPIE JMI 5(1), 015006–015006 (2018)
Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605–613. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_67
Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408–416. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_47
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
Wang, Z., Voiculescu, I. (2023). Exigent Examiner and Mean Teacher: An Advanced 3D CNN-Based Semi-Supervised Brain Tumor Segmentation Framework. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-44917-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47196-4
Online ISBN: 978-3-031-44917-8
eBook Packages: Computer ScienceComputer Science (R0)