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

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. 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. 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. 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.