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A Domain Knowledge-Based Semi-supervised Pancreas Segmentation Approach

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Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14450))

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Abstract

The five-year survival rate of pancreatic cancer is extremely low, and the survival time of patients can be extended by timely detection and treatment. Deep learning-based methods have been used to assist radiologists in diagnosis, with remarkable achievements. However, obtaining sufficient labeled data is time-consuming and labor-intensive. Semi-supervised learning is an effective way to alleviate dependence on annotated data by combining unlabeled data. Since the existing semi-supervised pancreas segmentation works are easier to ignore the domain knowledge, leading to location and shape bias. In this paper, we propose a semi-supervised pancreas segmentation method based on domain knowledge. Specifically, the prior constraints for different organ sub-regions are used to guide the pseudo-label generation for unlabeled data. Then the bidirectional information flow regularization is designed by further utilizing pseudo-labels, encouraging the model to align the labeled and unlabeled data distributions. Extensive experiments on NIH pancreas datasets show: the proposed method achieved Dice of 76.23% and 80.76% under 10% and 20% labeled data, respectively, which is superior to other semi-supervised pancreas segmentation methods.

Supported by National Natural Science Foundation of China (61976106, 62276116); Jiangsu Six Talent Peak Program (DZXX−122); Jiangsu Graduate Research Innovation Program (KYCX23_3677).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (61976106, 62276116); Jiangsu Six Talent Peak Program (DZXX−122); Jiangsu Graduate Research Innovation Program (KYCX23_3677). Thanks to the open source code from Luo et al.: https://github.com/HiLab-git/SSL4MIS.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Siqi Ma, Zhe Liu, Yuqing Song, Yi Liu, Kai Han & Yang Jiang

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  1. Siqi Ma
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  2. Zhe Liu
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  3. Yuqing Song
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  4. Yi Liu
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  5. Kai Han
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Corresponding author

Correspondence to Zhe Liu .

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Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Ma, S., Liu, Z., Song, Y., Liu, Y., Han, K., Jiang, Y. (2024). A Domain Knowledge-Based Semi-supervised Pancreas Segmentation Approach. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14450. Springer, Singapore. https://doi.org/10.1007/978-981-99-8070-3_6

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  • DOI: https://doi.org/10.1007/978-981-99-8070-3_6

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