Dataset Information

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.

ABSTRACT: Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry.

SUBMITTER: Cui Z

PROVIDER: S-EPMC9018763 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.

Cui Zhiming Z Fang Yu Y Mei Lanzhuju L Zhang Bojun B Yu Bo B Liu Jiameng J Jiang Caiwen C Sun Yuhang Y Ma Lei L Huang Jiawei J Liu Yang Y Zhao Yue Y Lian Chunfeng C Ding Zhongxiang Z Zhu Min M Shen Dinggang D

Nature communications 20220419 1

Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. This fully automatic AI system achieves a segmentati ...[more]

PMID: 35440592

Similar Datasets

Project description:The gray values accuracy of dental cone-beam computed tomography (CBCT) is affected by dental metal prostheses. The distortion of dental CBCT gray values could lead to inaccuracies of orthodontic and implant treatment. The aim of this study was to quantify the effect of scanning parameters and dental metal prostheses on the accuracy of dental cone-beam computed tomography (CBCT) gray values using the Taguchi method. Eight dental model casts of an upper jaw including prostheses, and a ninth prosthesis-free dental model cast, were scanned by two dental CBCT devices. The mean gray value of the selected circular regions of interest (ROIs) were measured using dental CBCT images of eight dental model casts and were compared with those measured from CBCT images of the prosthesis-free dental model cast. For each image set, four consecutive slices of gingiva were selected. The seven factors (CBCTs, occlusal plane canting, implant connection, prosthesis position, coping material, coping thickness, and types of dental restoration) were used to evaluate scanning parameter and dental prostheses effects. Statistical methods of signal to noise ratio (S/N) and analysis of variance (ANOVA) with 95% confidence were applied to quantify the effects of scanning parameters and dental prostheses on dental CBCT gray values accuracy. For ROIs surrounding dental prostheses, the accuracy of CBCT gray values were affected primarily by implant connection (42%), followed by type of restoration (29%), prostheses position (19%), coping material (4%), and coping thickness (4%). For a single crown prosthesis (without support of implants) placed in dental model casts, gray value differences for ROIs 1-9 were below 12% and gray value differences for ROIs 13-18 away from pros-theses were below 10%. We found the gray value differences set to be between 7% and 8% for regions next to a single implant-supported titanium prosthesis, and between 46% and 59% for regions between double implant-supported, nickel-chromium alloys (Ni-Cr) prostheses. Quantification of the effect of prostheses and scanning parameters on dental CBCT gray values was assessed.

Project description:ObjectiveTo determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection.Materials and methodsThis retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters.ResultsThe time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation.ConclusionVolumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.

Project description:PURPOSE:We evaluated the feasibility of using an automatic segmentation tool to delineate cardiac substructures from noncontrast computed tomography (CT) images for cardiac dosimetry and toxicity analyses for patients with nonsmall cell lung cancer (NSCLC) after radiotherapy. MATERIAL AND METHODS:We used an in-house developed multi-atlas segmentation tool to delineate 11cardiac substructures, including the whole heart, four heart chambers, and six greater vessels, automatically from the averaged 4D-CT planning images of 49 patients with NSCLC. Two experienced radiation oncologists edited the auto-segmented contours. Times for automatic segmentation and modification were recorded. The modified contours were compared with the auto-segmented contours in terms of Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate the extent of modification. Differences in dose-volume histogram (DVH) characteristics were also evaluated for the modified versus auto-segmented contours. RESULTS:The mean automatic segmentation time for all 11 structures was 7-9?min. For the 49 patients, the mean DSC values (±SD) ranged from .73?±?.08 to .95?±?.04, and the mean MSD values ranged from 1.3?±?.6?mm to 2.9?±?5.1?mm. Overall, the modifications were small; the largest modifications were in the pulmonary vein and the inferior vena cava. The heart V30 (volume receiving dose ?30?Gy) and the mean dose to the whole heart and the four heart chambers were not different for the modified versus the auto-segmented contours based on the statistically significant condition of p?<?.05. Also, the maximum dose to the great vessels was no different except for the pulmonary vein. CONCLUSIONS:Automatic segmentation of cardiac substructures did not require substantial modifications. Dosimetric evaluation showed no significant difference between the auto-segmented and modified contours for most structures, which suggests that the auto-segmented contours can be used to study cardiac dose-responses in clinical practice.

Dataset Information

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.

Publications

A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets