法医学杂志2025，Vol.41Issue(3)：208-216,9.DOI:10.12116/j.issn.1004-5619.2025.250106
基于分割标签与原始图像融合的双通道汉族青少年肩关节X线骨龄评估
Dual-Channel Shoulder Joint X-ray Bone Age Estimation in Chinese Han Ado-lescents Based on the Fusion of Segmentation Labels and Original Images
摘要
Abstract
Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.关键词
法医人类学/年龄推断/X线图像/肩关节/卷积神经网络/分割网络/青少年Key words
forensic anthropology/age estimation/X-ray image/shoulder joint/convolutional neural net-work/segmentation network/adolescents分类
医药卫生引用本文复制引用
周慧明,李丹阳,万雷,刘太昂,李远喆,汪茂文,王亚辉..基于分割标签与原始图像融合的双通道汉族青少年肩关节X线骨龄评估[J].法医学杂志,2025,41(3):208-216,9.基金项目
国家重点研发计划资助项目(2022YFC3302004) （2022YFC3302004）
国家自然科学基金资助项目(81571859,81102305) （81571859,81102305）
上海市2019年度"科技创新行动计划"技术标准项目(19DZ2201300) （19DZ2201300）
上海市法医学重点实验室资助项目(21DZ2270800) （21DZ2270800）
上海市司法鉴定专业技术服务平台资助项目 （）
司法部司法鉴定重点实验室资助项目 （）
