目标:开发基于深度学习的系统,健壮,以及在锥形束计算机断层扫描(CBCT)图像上对下颌管进行全自动分割。
方法:该系统是在一个中心的536次CBCT扫描(训练集:376,验证集:80,测试集:80)上开发的,并在来自3个中心的89次CBCT扫描的外部数据集上进行了验证。使用多阶段注释方法对每个扫描进行注释,并由口腔和颌面放射科医生进行细化。我们提出了下颌管分割的三步策略:基于2DU-Net提取感兴趣区域,下颌管的整体分割,基于3DU-Net的分割细化。
结果:系统在内部集中始终实现了准确的下颌管分割(Dice相似系数[DSC],0.952;在联合[IoU]上相交,0.912;平均对称表面距离[ASSD],0.046mm;95%Hausdorff距离[HD95],0.325毫米)和外部装置(DSC,0.960;IoU,0.924;ASSD,0.040毫米;HD95,0.288毫米)。
结论:这些结果证明了该AI系统在促进与下颌管定位相关的临床工作流程方面的潜在临床应用。
结论:在CBCT图像上准确描绘下颌管对于植入物的放置至关重要,下颌第三磨牙拔除,和正颌手术。该AI系统可实现跨不同模型的准确分割,这可能有助于更有效和精确的牙科自动化系统。
To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images.
The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net.
The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm).
These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization.
Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.