Kim Min Seok, Amm Elie, Parsi Goli, ElShebiny Tarek, Motro Melih
Department of Orthodontics and Dentofacial Orthopedics, Boston University Goldman School of Dentistry, Boston, Massachusetts.
Department of Orthodontics and Dentofacial Orthopedics, Boston University Goldman School of Dentistry, Boston, Massachusetts.
J World Fed Orthod. 2025 Apr;14(2):84-90. doi: 10.1016/j.ejwf.2024.09.008. Epub 2024 Nov 2.
Advancements in technology have led to the adoption of digital workflows in dentistry, which require the segmentation of regions of interest from cone-beam computed tomography (CBCT) scans. These segmentations assist in diagnosis, treatment planning, and research. However, manual segmentation is an expensive and labor-intensive process. Therefore, automated methods, such as convolutional neural networks (CNNs), provide a more efficient way to generate segmentations from CBCT scans.
A three-dimensional UNet-based CNN model, utilizing the Medical Image Segmentation CNN framework, was used for training and generating predictions from CBCT scans. A dataset of 351 CBCT scans, with ground-truth labels created through manual segmentation using AI-assisted segmentation software, was prepared. Data preprocessing, augmentation, and model training were performed, and the performance of the proposed CNN model was analyzed.
The CNN model achieved high accuracy in segmenting maxillary and mandibular teeth from CBCT scans, with average Dice Similarity Coefficient values of 91.83% and 91.35% for maxillary and mandibular teeth, respectively. Performance metrics, including Intersection over Union, precision, and recall, further confirmed the model's effectiveness.
The study demonstrates the efficacy of the three-dimensional UNet-based CNN model within the Medical Image Segmentation CNN framework for automated segmentation of maxillary and mandibular dentition from CBCT scans. Automated segmentation using CNNs has the potential to deliver accurate and efficient results, offering a significant advantage over traditional segmentation methods.
技术进步促使牙科采用数字工作流程，这需要从锥形束计算机断层扫描（CBCT）图像中分割出感兴趣区域。这些分割有助于诊断、治疗计划制定和研究。然而，手动分割是一个昂贵且劳动密集的过程。因此，诸如卷积神经网络（CNN）之类的自动化方法提供了一种从CBCT扫描生成分割的更有效方式。
基于三维U-Net的CNN模型，利用医学图像分割CNN框架，用于从CBCT扫描中进行训练和生成预测。准备了一个包含351张CBCT扫描的数据集，并使用人工智能辅助分割软件通过手动分割创建了真实标签。进行了数据预处理、增强和模型训练，并分析了所提出的CNN模型的性能。
CNN模型在从CBCT扫描中分割上颌和下颌牙齿方面取得了高精度，上颌牙齿和下颌牙齿的平均骰子相似系数值分别为91.83%和91.35%。包括交并比、精度和召回率在内的性能指标进一步证实了该模型的有效性。
该研究证明了基于三维U-Net的CNN模型在医学图像分割CNN框架内对从CBCT扫描中自动分割上颌和下颌牙列的有效性。使用CNN进行自动分割有潜力提供准确且高效的结果，相较于传统分割方法具有显著优势。
