Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.

IF 2.9 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

BMC Medical Imaging Pub Date : 2025-07-01 DOI:10.1186/s12880-025-01797-9

Ismail Gümüssoy, Emre Haylaz, Suayip Burak Duman, Fahrettin Kalabalik, Seyda Say, Ozer Celik, Ibrahim Sevki Bayrakdar

{"title":"Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.","authors":"Ismail Gümüssoy, Emre Haylaz, Suayip Burak Duman, Fahrettin Kalabalik, Seyda Say, Ozer Celik, Ibrahim Sevki Bayrakdar","doi":"10.1186/s12880-025-01797-9","DOIUrl":null,"url":null,"abstract":"Objective: This study aimed to identify the hyoid bone (HB) using the nnU-Net based artificial intelligence (AI) model in cone beam computed tomography (CBCT) images and assess the model's success in automatic segmentation.Methods: CBCT images of 190 patients were randomly selected. The raw data was converted to DICOM format and transferred to the 3D Slicer Imaging Software (Version 4.10.2; MIT, Cambridge, MA, USA). HB was labeled manually using the 3D Slicer. The dataset was divided into training, validation, and test sets in a ratio of 8:1:1. The nnU-Net v2 architecture was utilized to process the training and test datasets, generating the algorithm weight factors. To assess the model's accuracy and performance, a confusion matrix was employed. F1-score, Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) metrics were calculated to evaluate the results.Results: The model's performance metrics were as follows: DC = 0.9434, IoU = 0.8941, F1-score = 0.9446, and 95% HD = 1.9998. The receiver operating characteristic (ROC) curve was generated, yielding an AUC value of 0.98.Conclusion: The results indicated that the nnU-Net v2 model achieved high precision and accuracy in HB segmentation on CBCT images. Automatic segmentation of HB can enhance clinicians' decision-making speed and accuracy in diagnosing and treating various clinical conditions.Clinical trial number: Not applicable.","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"217"},"PeriodicalIF":2.9000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12880-025-01797-9","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Objective: This study aimed to identify the hyoid bone (HB) using the nnU-Net based artificial intelligence (AI) model in cone beam computed tomography (CBCT) images and assess the model's success in automatic segmentation.

Methods: CBCT images of 190 patients were randomly selected. The raw data was converted to DICOM format and transferred to the 3D Slicer Imaging Software (Version 4.10.2; MIT, Cambridge, MA, USA). HB was labeled manually using the 3D Slicer. The dataset was divided into training, validation, and test sets in a ratio of 8:1:1. The nnU-Net v2 architecture was utilized to process the training and test datasets, generating the algorithm weight factors. To assess the model's accuracy and performance, a confusion matrix was employed. F1-score, Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) metrics were calculated to evaluate the results.

Results: The model's performance metrics were as follows: DC = 0.9434, IoU = 0.8941, F1-score = 0.9446, and 95% HD = 1.9998. The receiver operating characteristic (ROC) curve was generated, yielding an AUC value of 0.98.

Conclusion: The results indicated that the nnU-Net v2 model achieved high precision and accuracy in HB segmentation on CBCT images. Automatic segmentation of HB can enhance clinicians' decision-making speed and accuracy in diagnosing and treating various clinical conditions.

Clinical trial number: Not applicable.

查看原文本刊更多论文

基于nnU-Net v2的CBCT舌骨自动三维分割：模型性能和潜在临床应用的回顾性研究

目的：利用基于nnU-Net的人工智能（AI）模型在锥形束计算机断层扫描（CBCT）图像中识别舌骨（HB），并评估该模型在自动分割中的成功程度。方法：随机选取190例患者的CBCT图像。将原始数据转换为DICOM格式，并传输到3D切片机成像软件(版本4.10.2；麻省理工学院，剑桥，马萨诸塞州，美国)。使用3D切片器手动标记HB。将数据集按8:1:1的比例分为训练集、验证集和测试集。利用nnU-Net v2架构对训练和测试数据集进行处理，生成算法权重因子。为了评估模型的准确性和性能，我们使用了一个混淆矩阵。计算f1评分、Dice系数（DC）、95% Hausdorff距离（95% HD）和Intersection over Union （IoU）指标来评估结果。结果：模型的性能指标为：DC = 0.9434, IoU = 0.8941, F1-score = 0.9446, 95% HD = 1.9998。生成受试者工作特征（ROC）曲线，AUC值为0.98。结论：nnU-Net v2模型对CBCT图像的HB分割具有较高的精度和准确性。HB的自动分割可以提高临床医生在诊断和治疗各种临床疾病时的决策速度和准确性。临床试验号：不适用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.60

自引率

3.70%

发文量

198

审稿时长

27 weeks

期刊介绍： BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.