目的:在数字牙科中越来越需要在颌面部(DMF)计算机断层扫描(CT)或锥形束计算机断层扫描(CBCT)扫描上分割解剖结构。这项研究的主要目的是提出和评估一种名为DentalSegmentator的新型开源工具,用于在DMFCT和CBCT扫描中对五个解剖结构进行全自动分割:上颌骨/上颅骨,下颌骨,上牙,下牙,和下颌管。
方法:使用470例CT和CBCT扫描的回顾性样本作为训练/验证集。通过比较专家提供的分割和两个固定测试数据集中的自动分割来评估工具的性能和通用性:在正颌手术之前获得的133个CT和CBCT扫描的内部数据集以及从5个机构的常规检查中随机抽样的123个CBCT扫描的外部数据集。
结果:内部测试数据集(n=133)中的平均总体结果是92.2±6.3%的Dice相似性系数(DSC)和98.2±2.2%的归一化表面距离(NSD)。外部测试数据集(n=123)的平均总体结果是94.2±7.4%的DSC和98.4±3.6%的NSD。
结论:从这个高度多样化的数据集获得的结果表明,该工具可以为DMFCT和CBCT扫描提供全自动和强大的多类别分割。为鼓励临床部署DentalSegmentator,预训练的nnU-Net模型已与3D切片器软件的扩展一起公开可用。
结论:DentalSegmentator开源3D切片器扩展提供了一个免费的,健壮,和易于使用的方法从CT和CBCT扫描获得患者特定的三维模型。这些模型在数字牙科工作流程中用于各种目的,比如可视化,治疗计划,干预,和后续行动。
OBJECTIVE: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal.
METHODS: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions.
RESULTS: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %.
CONCLUSIONS: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software.
CONCLUSIONS: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.