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This paper introduces a semi-supervised segmentation framework for maxillary sinus segmentation in panoramic X-ray images, using knowledge distillation to leverage both labeled and unlabeled data. A weighted knowledge distillation loss is proposed to mitigate unreliable distillation signals arising from structural differences between teacher and student models. Additionally, a SinusCycle-GAN refines pseudo-labels generated by the teacher network, improving boundary precision and reducing noise. Experiments on 2,511 patient images demonstrate a Dice score of 96.35%, outperforming state-of-the-art methods and showing robust performance with limited labeled data.
Achieve near-perfect (96.35% Dice) maxillary sinus segmentation from X-rays with limited labeled data by distilling knowledge from GAN-refined pseudo-labels.
Accurate segmentation of maxillary sinus in panoramic X-ray images is essential for dental diagnosis and surgical planning; however, this task remains relatively underexplored in dental imaging research. Structural overlap, ambiguous anatomical boundaries inherent to two-dimensional panoramic projections, and the limited availability of large scale clinical datasets with reliable pixel-level annotations make the development and evaluation of segmentation models challenging. To address these challenges, we propose a semi-supervised segmentation framework that effectively leverages both labeled and unlabeled panoramic radiographs, where knowledge distillation is utilized to train a student model with reliable structural information distilled from a teacher model. Specifically, we introduce a weighted knowledge distillation loss to suppress unreliable distillation signals caused by structural discrepancies between teacher and student predictions. To further enhance the quality of pseudo labels generated by the teacher network, we introduce SinusCycle-GAN which is a refinement network based on unpaired image-to-image translation. This refinement process improves the precision of boundaries and reduces noise propagation when learning from unlabeled data during semi-supervised training. To evaluate the proposed method, we collected clinical panoramic X-ray images from 2,511 patients, and experimental results demonstrate that the proposed method outperforms state-of-the-art segmentation models, achieving the Dice score of 96.35\% while reducing boundary error. The results indicate that the proposed semi-supervised framework provides robust and anatomically consistent segmentation performance under limited labeled data conditions, highlighting its potential for broader dental image analysis applications.
