Deep learning segmentation of mandible with lower dentition from cone beam CT.

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Daniel C Kargilis, Winnie Xu, Samir Reddy, Shilpa Shree Kuduva Ramesh, Steven Wang, Anh D Le, Chamith S Rajapakse
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引用次数: 0

Abstract

Objectives: This study aimed to train a 3D U-Net convolutional neural network (CNN) for mandible and lower dentition segmentation from cone-beam computed tomography (CBCT) scans.

Methods: In an ambispective cross-sectional design, CBCT scans from two hospitals (2009-2019 and 2021-2022) constituted an internal dataset and external validation set, respectively. Manual segmentation informed CNN training, and evaluations employed Dice similarity coefficient (DSC) for volumetric accuracy. A blinded oral maxillofacial surgeon performed qualitative grading of CBCT scans and object meshes. Statistical analyses included independent t-tests and ANOVA tests to compare DSC across patient subgroups of gender, race, body mass index (BMI), test dataset used, age, and degree of metal artifact. Tests were powered for a minimum detectable difference in DSC of 0.025, with alpha of 0.05 and power level of 0.8.

Results: 648 CBCT scans from 490 patients were included in the study. The CNN achieved high accuracy (average DSC: 0.945 internal, 0.940 external). No DSC differences were observed between test set used, gender, BMI, and race. Significant differences in DSC were identified based on age group and the degree of metal artifact. The majority (80%) of object meshes produced by both manual and automatic segmentation were rated as acceptable or higher quality.

Conclusion: We developed a model for automatic mandible and lower dentition segmentation from CBCT scans in a demographically diverse cohort including a high degree of metal artifacts. The model demonstrated good accuracy on internal and external test sets, with majority acceptable quality from a clinical grader.

Abstract Image

利用锥形束 CT 对下颌骨和下牙进行深度学习分割。
研究目的本研究旨在训练一个三维 U-Net 卷积神经网络(CNN),用于从锥形束计算机断层扫描(CBCT)中分割下颌骨和下牙列:方法:在一项前瞻性横断面设计中,来自两家医院(2009-2019 年和 2021-2022 年)的 CBCT 扫描分别构成内部数据集和外部验证集。人工分割为 CNN 训练提供了依据,评估采用了 Dice 相似性系数(DSC)来衡量体积准确性。一名口腔颌面外科医生对 CBCT 扫描和对象网格进行了盲法定性分级。统计分析包括独立 t 检验和方差分析检验,以比较不同性别、种族、体重指数 (BMI)、所用测试数据集、年龄和金属伪影程度的患者亚组的 DSC。测试的最小可检测到的 DSC 差异为 0.025,α 为 0.05,功率水平为 0.8:研究共纳入了 490 名患者的 648 次 CBCT 扫描。CNN 的准确度很高(平均 DSC:内部 0.945,外部 0.940)。所使用的测试集、性别、体重指数和种族之间未发现 DSC 差异。基于年龄组和金属伪装程度的 DSC 存在显著差异。大部分(80%)手动和自动分割生成的对象网格都被评为可接受或更高质量:我们开发了一个模型,用于从 CBCT 扫描结果中自动分割下颌骨和下牙槽骨。该模型在内部和外部测试集上都表现出了良好的准确性,临床评分员对其质量大多表示可以接受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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