Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA.
Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Illinois, USA.
Orthod Craniofac Res. 2023 Dec;26 Suppl 1:111-117. doi: 10.1111/ocr.12644. Epub 2023 Mar 13.
A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed.
A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters.
The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects.
AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.
提出了一种使用深度学习（DL）网络对颈椎成熟度（CVM）阶段进行监督自动分类的研究。提出了一种具有预处理层的并行结构深度卷积神经网络（CNN），该预处理层以 X 射线图像和年龄作为输入。
根据 CVM 阶段对总共 1018 张头影测量射线照片进行了标记和分类。为了更好地拟合模型，按性别对图像进行了分离。使用目标检测器自动裁剪图像以提取颈椎。将得到的图像和年龄输入用于训练所提出的 DL 模型：带有一组可调定向边缘增强器的 AggregateNet。提取图像的特征后，将年龄输入与输出特征向量连接起来。为了使并行网络不过拟合，使用了数据增强。将我们的 CNN 模型的性能与其他 DL 模型、ResNet20、Xception、MobileNetV2 和具有定向滤波器的自定义 CNN 模型进行了比较。
在所提出的创新模型中，使用了具有边缘增强滤波器预处理层的并行结构网络，在女性 CVM 阶段分类中验证准确率达到 82.35%，在男性 CVM 阶段分类中验证准确率达到 75.0%，优于所研究的其他 DL 模型的准确率。定向滤波器的有效性反映在改进的性能中。如果不使用定向滤波器使用 AggregateNet，则女性受试者的测试准确率降至 80.0%，男性受试者的测试准确率降至 74.03%。
与我们研究的其他模型相比，AggregateNet 与可调定向边缘滤波器相结合，在 CVM 阶段的全自动确定中表现出更高的准确性。
