目的:本研究的目的是开发一种深度学习(DL)模型,用于在锥形束计算机断层扫描(CBCT)上快速,准确地分割下颌管(MC)。
方法:本研究共使用220例需要口腔手术的齿状受试者的CBCT扫描。分割基础事实由两名高级牙医注释和审查。所有患者都被随机分成一个训练数据集(n=132),验证数据集(n=44)和测试数据集(n=44)。我们提出了一个基于两阶段3D-UNet的分割框架,用于CBCT上的自动MC分割。使用骰子相似系数(DSC)和95%Hausdorff距离(95%HD)作为分割模型的评估度量。
结果:两阶段3D-UNet模型成功分割了CBCT图像上的MC。在测试数据集中,平均DSC为0.875±0.045,平均95%HD为0.442±0.379。
结论:这种自动DL方法可能有助于检测MC,并帮助牙科医生建立口腔外科进化MC的治疗计划。
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.