Abstract
Although deep learning models have demonstrated impressive performance in various biomedical image segmentation tasks, their effectiveness heavily relies on a large amount of annotated training data, which can be costly to acquire. Semi-supervised learning (SSL) methods have emerged as a potential solution to mitigate this challenge by leveraging the abundance of unlabeled data. In this paper, we propose a highly effective SSL method for 3D biomedical image segmentation, called Pyramid Pseudo-Labeling Supervision (PPS). The PPS comprises three segmentation networks, forming a pyramid-like network structure. To enforce consistency in the outputs of the unlabeled data, we introduce a novel rectified pyramid consistency (RPC) loss. The PPS learns from the plentiful unlabeled data by minimizing the RPC loss, which ensures consistency between the pyramid predictions and the cycled pseudo-labeling knowledge among the three segmentation networks. Additionally, weak data augmentation is applied to perturb the inputs, further enhancing the consistency of the unlabeled data outputs. Experimental results demonstrate that our method achieves state-of-the-art performance on two publicly available 3D biomedical image datasets.
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
Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613–2622 (2021)
Gao, F., et al.: Segmentation only uses sparse annotations: unified weakly and semi-supervised learning in medical images. Med. Image Anal. 80, 102515 (2022)
Lin, X., Zhou, X., Tong, T., Nie, X., Li, Z.: SG-Net: a super-resolution guided network for improving thyroid nodule segmentation. In: 2022 IEEE 24th International Conference on High Performance Computing & Communications; 8th International Conference on Data Science & Systems; 20th International Conference on Smart City; 8th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), pp. 1770–1775. IEEE (2022)
Lin, X., et al.: A super-resolution guided network for improving automated thyroid nodule segmentation. Comput. Methods Programs Biomed. 227, 107186 (2022)
Louis, D.N., et al.: The 2007 who classification of tumours of the central nervous system. Acta Neuropathol. 114, 97–109 (2007)
Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8801–8809 (2021)
Luo, X., et al.: Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Med. Image Anal. 80, 102517 (2022)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Nie, X., et al.: N-Net: a novel dense fully convolutional neural network for thyroid nodule segmentation. Front. Neurosci. 16, 872601 (2022)
Njoku, A., et al.: Left atrial volume predicts atrial fibrillation recurrence after radiofrequency ablation: a meta-analysis. EP Europace 20(1), 33–42 (2018)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: eight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Vu, T.H., Jain, H., Bucher, M., Cord, M., Pérez, P.: ADVENT: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517–2526 (2019)
Xiong, Z., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832 (2021)
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, D., Chen, B., Chong, J., Li, S.: Weakly-supervised teacher-student network for liver tumor segmentation from non-enhanced images. Med. Image Anal. 70, 102005 (2021)
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., 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, pp. 408–416. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_47
Zheng, H., Zhou, X., Li, J., Gao, Q., Tong, T.: White blood cell segmentation based on visual attention mechanism and model fitting. In: 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC), pp. 47–50. IEEE (2020)
Zhong, Z., Wang, T., Zeng, K., Zhou, X., Li, Z.: White blood cell segmentation via sparsity and geometry constraints. IEEE Access 7, 167593–167604 (2019)
Zhou, X., Li, Z., Tong, T.: DTSC-Net: semi-supervised 3D biomedical image segmentation through dual-teacher simplified consistency. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1429–1434. IEEE (2022)
Zhou, X., Li, Z., Tong, T.: DM-Net: a dual-model network for automated biomedical image diagnosis. In: Tang, H. (eds.) International Conference on Research in Computational Molecular Biology, RECOMB 2023. LNCS, vol. 13976, pp. 74–84. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-29119-7_5
Zhou, X., et al.: CUSS-Net: a cascaded unsupervised-based strategy and supervised network for biomedical image diagnosis and segmentation. IEEE J. Biomed. Health Inform. 27(5), 2444–2455 (2023)
Zhou, X., et al.: Leukocyte image segmentation based on adaptive histogram thresholding and contour detection. Curr. Bioinform. 15(3), 187–195 (2020)
Zhou, X., et al.: H-Net: a dual-decoder enhanced FCNN for automated biomedical image diagnosis. Inf. Sci. 613, 575–590 (2022)
Zhou, X., Tong, T., Zhong, Z., Fan, H., Li, Z.: Saliency-CCE: exploiting colour contextual extractor and saliency-based biomedical image segmentation. Comput. Biol. Med. 154, 106551 (2023)
Zhou, X., Wang, C., Li, Z., Zhang, F.: Adaptive histogram thresholding-based leukocyte image segmentation. In: Pan, J.S., Li, J., Tsai, P.W., Jain, L. (eds.) Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceedings of the 15th International Conference on IIH-MSP in Conjunction with the 12th International Conference on FITAT, 18–20 July 2020, Jilin, China, vol. 2, pp. 451–459. Springer, Cham (2020). https://doi.org/10.1007/978-981-13-9710-3_47
Zhuang, X., Li, Y., Hu, Y., Ma, K., Yang, Y., Zheng, Y.: Self-supervised feature learning for 3D medical images by playing a Rubik’s cube. In: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2019: 22nd International Conference, Shenzhen, China, 13–17 October 2019, Proceedings, Part IV 22, vol. 11767, pp. 420–428. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_46
Acknowledgment
This work was supported by National Natural Science Foundation of China under Grant 62171133, in part by the Science and Technology Innovation Joint Fund Program of Fujian Province of China under Grant 2019Y9104, the Health Science and Technology Program of Fujian Province of China under Grant 2019-1-33, and the Industry-University-Research Cooperation Program of Fujian Province of China under Grant 2022H6006.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, X., Li, Z., Tong, T. (2024). PPS: Semi-supervised 3D Biomedical Image Segmentation via Pyramid Pseudo-Labeling Supervision. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_23
Download citation
DOI: https://doi.org/10.1007/978-981-99-8558-6_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8557-9
Online ISBN: 978-981-99-8558-6
eBook Packages: Computer ScienceComputer Science (R0)