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Background <p>Accurate segmentation of anatomical and pathological structures in periapical radiographs is essential for&#xa0;digital dentistry, yet obtaining sufficient manual annotations remains a major challenge. This study aims to develop a hybrid self-supervised and semi-supervised learning framework to address the data annotation bottleneck in multi-class structure segmentation of dental periapical films.</p> Method <p>We propose a two-stage approach combining self-supervised learning with semi-supervised fine-tuning. First, we employ an Intensity-Gradient-Local Contrast based Masked Autoencoder (IGLC-MAE) for self-supervised learning on 74,292 unlabeled periapical films, utilizing structured adaptive masking specifically designed for dental radiography characteristics.&#xa0;The pre-trained model then generates pseudo-labels for 6,259 unlabeled images, which are combined with 229 manual annotations for semi-supervised fine-tuning using Mask2Former. To optimize this process, we systematically evaluated loss weight configurations and introduced an adaptive weighting mechanism, which together improved the quality of pseudo-labels and further enhanced segmentation performance.</p> Results <p>Compared to traditional supervised learning methods, our self-supervised learning approach achieved significant improvements in oral structure segmentation, with a Dice score of 73.17%, surpassing the best supervised learning configuration by 10.52%. Subsequently, the semi-supervised learning strategy with pseudo-labels further enhanced performance, reaching the highest Dice score of 74.12% at an optimal weight ratio of 0.85:0.15 for the manual-to-pseudo-label loss. The adaptive semi-supervised strategy delivered an additional 1.23% improvement in Dice score by effectively suppressing low-confidence pseudo-label noise.</p> Conclusion <p>The proposed hybrid self-supervised and semi-supervised framework. effectively addresses the challenge of annotation data scarcity and provides a new technical approach for dental image analysis. Our method achieves superior segmentation performance in periapical films while minimizing dependency on manual annotations, offering a clinically viable solution for medical imaging.</p>

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A paradigm of hybrid-supervision for annotation-scarce periapical film analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Qianli Zhang,
  • Meiyu Hu,
  • Mu Yuan,
  • Pingyi Jia,
  • Huajie Yu,
  • Feng Chen,
  • Xu-Cheng Yin,
  • Junran Peng

摘要

Background

Accurate segmentation of anatomical and pathological structures in periapical radiographs is essential for digital dentistry, yet obtaining sufficient manual annotations remains a major challenge. This study aims to develop a hybrid self-supervised and semi-supervised learning framework to address the data annotation bottleneck in multi-class structure segmentation of dental periapical films.

Method

We propose a two-stage approach combining self-supervised learning with semi-supervised fine-tuning. First, we employ an Intensity-Gradient-Local Contrast based Masked Autoencoder (IGLC-MAE) for self-supervised learning on 74,292 unlabeled periapical films, utilizing structured adaptive masking specifically designed for dental radiography characteristics. The pre-trained model then generates pseudo-labels for 6,259 unlabeled images, which are combined with 229 manual annotations for semi-supervised fine-tuning using Mask2Former. To optimize this process, we systematically evaluated loss weight configurations and introduced an adaptive weighting mechanism, which together improved the quality of pseudo-labels and further enhanced segmentation performance.

Results

Compared to traditional supervised learning methods, our self-supervised learning approach achieved significant improvements in oral structure segmentation, with a Dice score of 73.17%, surpassing the best supervised learning configuration by 10.52%. Subsequently, the semi-supervised learning strategy with pseudo-labels further enhanced performance, reaching the highest Dice score of 74.12% at an optimal weight ratio of 0.85:0.15 for the manual-to-pseudo-label loss. The adaptive semi-supervised strategy delivered an additional 1.23% improvement in Dice score by effectively suppressing low-confidence pseudo-label noise.

Conclusion

The proposed hybrid self-supervised and semi-supervised framework. effectively addresses the challenge of annotation data scarcity and provides a new technical approach for dental image analysis. Our method achieves superior segmentation performance in periapical films while minimizing dependency on manual annotations, offering a clinically viable solution for medical imaging.