Clinic of Stomatology of the Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangdong, China.
Department of Stomatology of Shantou University Medical College, No. 22, Xinling Road, Shantou, Guangddong, China.
BMC Oral Health. 2023 Aug 10;23(1):551. doi: 10.1186/s12903-023-03279-2.
The objective of this study is to develop a deep learning (DL) model for fast and accurate mandibular canal (MC) segmentation on cone beam computed tomography (CBCT).
A total of 220 CBCT scans from dentate subjects needing oral surgery were used in this study. The segmentation ground truth is annotated and reviewed by two senior dentists. All patients were randomly splitted into a training dataset (n = 132), a validation dataset (n = 44) and a test dataset (n = 44). We proposed a two-stage 3D-UNet based segmentation framework for automated MC segmentation on CBCT. The Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD) were used as the evaluation metrics for the segmentation model.
The two-stage 3D-UNet model successfully segmented the MC on CBCT images. In the test dataset, the mean DSC was 0.875 ± 0.045 and the mean 95% HD was 0.442 ± 0.379.
This automatic DL method might aid in the detection of MC and assist dental practitioners to set up treatment plans for oral surgery evolved MC.
本研究旨在开发一种基于深度学习（DL）的方法，以便在锥形束计算机断层扫描（CBCT）上快速准确地分割下颌管（MC）。
本研究共使用了 220 例需要口腔手术的有牙患者的 CBCT 扫描。分段的真实情况由两名资深牙医进行注释和审查。所有患者均随机分为训练数据集（n=132）、验证数据集（n=44）和测试数据集（n=44）。我们提出了一种基于两阶段 3D-UNet 的分割框架，用于在 CBCT 上自动分割 MC。Dice 相似系数（DSC）和 95%Hausdorff 距离（95%HD）用作分割模型的评估指标。
两阶段 3D-UNet 模型成功地对 CBCT 图像上的 MC 进行了分割。在测试数据集上，平均 DSC 为 0.875±0.045，平均 95%HD 为 0.442±0.379。
这种自动深度学习方法可以帮助检测 MC，并帮助牙科医生为口腔手术中发生变化的 MC 制定治疗计划。
