Süküt Yağızalp, Yurdakurban Ebru, Duran Gökhan Serhat
Department of Orthodontics, Gülhane Faculty of Dentistry, University of Health Sciences, Ankara 06010, Turkey.
Department of Orthodontics, Faculty of Dentistry, Muğla Sıtkı Koçman University, Muğla 48000, Turkey.
J Stomatol Oral Maxillofac Surg. 2025 Mar;126(2):102048. doi: 10.1016/j.jormas.2024.102048. Epub 2024 Sep 5.
In orthodontic treatments, accurately assessing the upper airway volume and morphology is essential for proper diagnosis and planning. Cone beam computed tomography (CBCT) is used for assessing upper airway volume through manual, semi-automatic, and automatic airway segmentation methods. This study evaluates upper airway segmentation accuracy by comparing the results of an automatic model and a semi-automatic method against the gold standard manual method.
An automatic segmentation model was trained using the MONAI Label framework to segment the upper airway from CBCT images. An open-source program, ITK-SNAP, was used for semi-automatic segmentation. The accuracy of both methods was evaluated against manual segmentations. Evaluation metrics included Dice Similarity Coefficient (DSC), Precision, Recall, 95% Hausdorff Distance (HD), and volumetric differences.
The automatic segmentation group averaged a DSC score of 0.915±0.041, while the semi-automatic group scored 0.940±0.021, indicating clinically acceptable accuracy for both methods. Analysis of the 95% HD revealed that semi-automatic segmentation (0.997±0.585) was more accurate and closer to manual segmentation than automatic segmentation (1.447±0.674). Volumetric comparisons revealed no statistically significant differences between automatic and manual segmentation for total, oropharyngeal, and velopharyngeal airway volumes. Similarly, no significant differences were noted between the semi-automatic and manual methods across these regions.
It has been observed that both automatic and semi-automatic methods, which utilise open-source software, align effectively with manual segmentation. Implementing these methods can aid in decision-making by allowing faster and easier upper airway segmentation with comparable accuracy in orthodontic practice.
在正畸治疗中，准确评估上气道容积和形态对于正确诊断和治疗计划至关重要。锥形束计算机断层扫描（CBCT）通过手动、半自动和自动气道分割方法用于评估上气道容积。本研究通过将自动模型和半自动方法的结果与金标准手动方法进行比较，评估上气道分割的准确性。
使用MONAI Label框架训练自动分割模型，以从CBCT图像中分割上气道。一个开源程序ITK-SNAP用于半自动分割。两种方法的准确性均与手动分割进行评估。评估指标包括骰子相似系数（DSC）、精确率、召回率、95%豪斯多夫距离（HD）和容积差异。
自动分割组的DSC评分平均为0.915±0.041，而半自动组的评分为0.940±0.021，表明两种方法在临床上均可接受。对95%HD的分析表明，半自动分割（0.997±0.585）比自动分割（1.447±0.674）更准确，更接近手动分割。容积比较显示，自动分割与手动分割在总气道、口咽气道和腭咽气道容积方面无统计学显著差异。同样，在这些区域中，半自动方法与手动方法之间也未观察到显著差异。
已经观察到，使用开源软件的自动和半自动方法都能有效地与手动分割对齐。在正畸实践中实施这些方法可以通过更快、更容易地进行上气道分割并具有可比的准确性来辅助决策。
