Objective

3D Digitization of dental model is growing in popularity for dental application. Classification of tooth type from single 3D point cloud model without assist of relative position among teeth is still a challenging task.

Methods

In this paper, 8-class posterior tooth type classification (first premolar, second premolar, first molar, second molar in maxilla and mandible respectively) was investigated by convolutional neural network (CNN)-based occlusal surface morphology analysis. 3D occlusal surface was transformed to depth image for basic CNN-based classification. Considering the logical hierarchy of tooth categories, a hierarchical classification structure was proposed to decompose 8-class classification task into two-stage cascaded classification subtasks. Image augmentations including traditional geometrical transformation and deep convolutional generative adversarial networks (DCGANs) were applied for each subnetworks and cascaded network.

Results

Results indicate that combing traditional and DCGAN-based augmented images to train CNN models can improve classification performance. In the paper, we achieve overall accuracy 91.35%, macro precision 91.49%, macro-recall 91.29%, and macro-F1 0.9139 for the 8-class posterior tooth type classification, which outperform other deep learning models. Meanwhile, Grad-cam results demonstrate that CNN model trained by our augmented images will focus on smaller important region for better generality. And anatomic landmarks of cusp, fossa, and groove work as important regions for cascaded classification model.

Conclusion

The reported work has proved that using basic CNN to construct two-stage hierarchical structure can achieve the best classification performance of posterior tooth type in 3D model without assistance of relative position information. The proposed method has advantages of easy training, great ability to learn discriminative features from small image region.

中文翻译：

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基于 CNN 的分层咬合面形态分析，使用来自 3D 牙齿表面模型的增强图像对后牙类型进行分类

客观的

牙科模型的 3D 数字化在牙科应用中越来越受欢迎。在没有牙齿之间相对位置的帮助下，从单个 3D 点云模型对牙齿类型进行分类仍然是一项具有挑战性的任务。

方法

在本文中，通过基于卷积神经网络（CNN）的咬合面形态分析研究了 8 类后牙类型分类（分别位于上颌和下颌的第一前磨牙、第二前磨牙、第一磨牙、第二磨牙）。3D 咬合面被转换为深度图像，用于基于 CNN 的基本分类。考虑到牙齿类别的逻辑层次，提出了一种层次分类结构，将8类分类任务分解为两阶段级联分类子任务。包括传统几何变换和深度卷积生成对抗网络 (DCGAN) 在内的图像增强被应用于每个子网络和级联网络。

结果

结果表明，结合传统和基于 DCGAN 的增强图像来训练 CNN 模型可以提高分类性能。在论文中，我们实现了 8 类后牙类型分类的总体准确率 91.35%、宏观准确率 91.49%、宏观召回率 91.29% 和 macro-F1 0.9139，优于其他深度学习模型。同时，Grad-cam 结果表明，由我们的增强图像训练的 CNN 模型将专注于较小的重要区域，以获得更好的通用性。尖、窝和沟的解剖标志是级联分类模型的重要区域。

结论

报道的工作已经证明，在没有相对位置信息的帮助下，使用基本的 CNN 构建两阶段层次结构可以在 3D 模型中实现后牙类型的最佳分类性能。该方法具有易于训练、从小图像区域学习判别特征的能力强等优点。