背景:上颌骨扩张术是治疗上颌骨横向发育不全的重要方法。上颌骨扩张的方法应根据腭中缝成熟水平进行,正畸医生通过腭平面锥形束计算机断层扫描(CBCT)图像验证了诊断,而这种方法效率低、主观性强。这项研究开发并评估了一种增强的视觉转换器(ViT),以自动对具有不同成熟阶段的中腭缝合线的CBCT图像进行分类。
方法:近年来,利用卷积神经网络(CNN)对不同成熟阶段的腭中缝图像进行分类,对临床上颌扩张方法的决策具有积极意义。然而,CNN无法充分了解图像和特征之间的长距离依赖关系,这也是全局识别腭中缝CBCT图像所必需的。ViT的自注意具有捕获图像的长距离像素之间的关系的功能。然而,它缺乏CNN的归纳偏差,需要更多的数据训练。为了解决这个问题,提出了一种基于迁移学习的CNN增强ViT模型对腭中缝CBCT图像进行分类。在这项研究中,收集2518张腭平面的CBCT图像,并将图像分为1259个图像作为训练集,506图像作为验证集,和753图像作为测试集。训练集图像预处理后,对CNN增强的ViT模型进行训练和调整,并在测试集上对模型的泛化能力进行了测试。
结果:我们提出的ViT模型的分类精度为95.75%,在我们的数据测试集上,其在接收器工作特征曲线(AUC)下的宏观平均面积和微观平均AUC分别为97.89%和98.36%。在我们的数据测试集上,表现最好的CNN模型EfficientnetV2_S的分类准确率为93.76%。在我们的数据测试集上,临床医生的分类准确率为89.10%。
结论:实验结果表明,该方法可以有效地完成cbc中缝成熟阶段的CBCT图像分类,表现比临床医生好。因此,该模型可以为正畸医生提供有价值的参考,并帮助他们做出正确的诊断。
BACKGROUND: Maxillary expansion is an important treatment method for maxillary transverse hypoplasia. Different methods of maxillary expansion should be carried out depending on the midpalatal suture maturation levels, and the diagnosis was validated by palatal plane cone beam computed tomography (CBCT) images by orthodontists, while such a method suffered from low efficiency and strong subjectivity. This study develops and evaluates an enhanced vision transformer (ViT) to automatically classify CBCT images of midpalatal sutures with different maturation stages.
METHODS: In recent years, the use of convolutional neural network (CNN) to classify images of midpalatal suture with different maturation stages has brought positive significance to the decision of the clinical maxillary expansion method. However, CNN cannot adequately learn the long-distance dependencies between images and features, which are also required for global recognition of midpalatal suture CBCT images. The Self-Attention of ViT has the function of capturing the relationship between long-distance pixels of the image. However, it lacks the inductive bias of CNN and needs more data training. To solve this problem, a CNN-enhanced ViT model based on transfer learning is proposed to classify midpalatal suture CBCT images. In this study, 2518 CBCT images of the palate plane are collected, and the images are divided into 1259 images as the training set, 506 images as the verification set, and 753 images as the test set. After the training set image preprocessing, the CNN-enhanced ViT model is trained and adjusted, and the generalization ability of the model is tested on the test set.
RESULTS: The classification accuracy of our proposed ViT model is 95.75%, and its Macro-averaging Area under the receiver operating characteristic Curve (AUC) and Micro-averaging AUC are 97.89% and 98.36% respectively on our data test set. The classification accuracy of the best performing CNN model EfficientnetV2_S was 93.76% on our data test set. The classification accuracy of the clinician is 89.10% on our data test set.
CONCLUSIONS: The experimental results show that this method can effectively complete CBCT images classification of midpalatal suture maturation stages, and the performance is better than a clinician. Therefore, the model can provide a valuable reference for orthodontists and assist them in making correct a diagnosis.