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Exploring refined boundaries and accurate pseudo-labels for semi-supervised medical image segmentation

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Abstract

Due to the tedious and expensive nature of annotating data for medical image segmentation tasks, semi-supervised learning (SSL) methods utilizing a small amount of labeled data have gained widespread attention. However, most existing methods overlook the importance of boundary regions in multi-class tasks and are sensitive to incorrect pseudo-label. In this paper, a novel Dual-Cycled Boundary Refine Network (DCBR-Net) is presented, consisting of two simple yet slightly different segmentation networks and a Boundary Residual Refine (BRR) module. First, a new boundary refine method for the semi-supervised medical image segmentation field is designed, that is a BRR module with a residual architecture. This module is capable of enhancing the representation of boundaries while ensuring the quality of inner regions. Besides, a dual-cycled pseudo-label scheme is designed to train the unlabeled data. Through providing diverse outputs and achieving double supervision for the result, the issue of ineffective guidance caused by model consistency after multiple iterations is alleviated. Furthermore, a novel dynamic loss function is developed based on the prediction disagreement between different models, which can suppress the influence of incorrect pseudo-labels. Extensive experiments conducted on four public medical datasets demonstrate that our network can achieve competitive results, especially with higher reliability in boundary regions. On the ACDC, LA, and Fundus datasets, with only a 20% labeled ratio, our network achieves DSC scores of 90.53%, 90.40%, and 89.19%, respectively, which are comparable to fully supervised performance.

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Availability of data

All benchmark datasets are freely available from [45, 46, 48,49,50].

Code availability

Code availability not applicable.

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Acknowledgements

The work was supported in part by the National Natural Science Foundation of China (No. 62272027), and the Beijing Natural Science Foundation (No. 4232012).

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Xiaochen Ma: Conceptualization, Methodology, Software, Writing-Original draft preparation. Yanfeng Li: Conceptualization, Resources, Supervision, Writing-Reviewing and Editing. Jia Sun: Conceptualization, Supervision, Writing-Reviewing and Editing. Houjin Chen: Conceptualization, Supervision, Writing-Reviewing and Editing. Yihan Ren: Conceptualization, Supervision, Writing-Reviewing and Editing. Ziwei Chen: Conceptualization, Supervision, Writing-Reviewing and Editing.

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Correspondence to Yanfeng Li.

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Ma, X., Li, Y., Sun, J. et al. Exploring refined boundaries and accurate pseudo-labels for semi-supervised medical image segmentation. Appl Intell 55, 246 (2025). https://doi.org/10.1007/s10489-024-06222-2

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