基于多源半监督学习的牙齿结构分割  

Teeth Structure Segmentation Based on Multi-Source Semi-Supervised Learning

作　　者：贾子昂 JIA Ziang(School of Computer,Hunan University of Technology,Zhuzhou,Hunan 412007,China)

机构地区：[1]湖南工业大学,计算机学院,湖南株洲412007

出　　处：《数据与计算发展前沿(中英文)》2025年第2期175-185,共11页Frontiers of Data & Computing

摘　　要：【目的】现有的牙齿结构分割研究中,牙齿数据具有成本高昂和来源复杂的特点,研究人员普遍选择在多源数据集上使用半监督方法构建深度学习网络,而从多源未标注数据中学习差异成为新的挑战。【方法】本研究以半监督算法Mean-Teacher为核心,提出一种多源半监督算法,并使用新的损失函数计算一致性损失,旨在更好地帮助半监督模型进行训练。【结果】在真实临床场景中收集的测试数据集上评估了这一方法的分割性能,结果表明,本文提出的方法在整体牙齿上的分割结果为平均Dice系数81.13%,平均Jaccard系数82.96%,平均召回率82.78%,平均精确度82.91%,与其他方法相比获得了更准确的分割性能。【结论】比起直接将未标注数据统一投入训练的主流方法,基于多源半监督学习的牙齿分割模型Multi-Student可以更好地帮助半监督模型提高精度。[Objective]Current research in dental structure segmentation faces two primary constraints:the prohibitively high costs of annotated dental data and the inherent complexity of multisource datasets.While semi-supervised learning approaches have been widely adopted for constructing deep learning networks using multi-source data,effectively extracting discriminative features from heterogeneous unlabeled data remains a significant challenge.[Methods]Building upon the Mean-Teacher framework,we propose a novel Multi-Source Semi-Supervised Learning algorithm that incorporates,a domain-adaptive consistency regularization mechanism,and a hybrid loss function combining structural similarity and uncertainty-aware constraints to optimize model training.[Results]Comprehensive evaluation on a clinically acquired dataset demonstrated superior segmentation performance,achieving mean scores of:Dice 81.13%,Jaccard 82.96%,recall 82.78%,and precision 82.91%.Statistical analysis confirmed significant improvements over other methods.[Conclusions]Compared to mainstream methods that directly incorporate unlabeled data into training,the Multi-Student model,based on multi-source semi-supervised learning,can better enhance the accuracy of semi-supervised tooth segmentation models.

关 键 词：人工智能 医学影像 迁移学习 深度学习 牙齿分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]