背景:在正畸治疗中,准确评估上呼吸道容积和形态对于正确诊断和规划至关重要.锥形束计算机断层扫描(CBCT)用于通过手动评估上气道容积,半自动,和自动气道分割方法。本研究通过将自动模型和半自动方法的结果与黄金标准手动方法进行比较来评估上呼吸道分割的准确性。
方法:使用MONAI标签框架训练自动分割模型,以从CBCT图像中分割上呼吸道。一个开源程序,ITK-SNAP,用于半自动分割。两种方法的准确性均通过手动分割进行评估。评估指标包括骰子相似系数(DSC)、Precision,回想一下,95%Hausdorff距离(HD),和体积差异。
结果:自动分割组的平均DSC评分为0.915±0.041,而半自动组的评分为0.940±0.021,表明两种方法的临床准确性均可接受。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。体积比较显示,自动和手动分割之间没有统计学上的显着差异,口咽,咽喉气道容积.同样,这些地区的半自动和手动方法没有显著差异.
结论:已经观察到,自动和半自动方法,利用开源软件,与手动分割有效对齐。实施这些方法可以通过允许在正畸实践中具有可比的准确性的更快,更容易的上气道分割来帮助决策。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.