法医学杂志2024，Vol.40Issue(2)：154-163,10.DOI:10.12116/j.issn.1004-5619.2023.231003
CT三维重建技术结合深度学习算法推断成人坐骨年龄
Adults Ischium Age Estimation Based on Deep Learning and 3D CT Reconstruction
摘要
Abstract
Objective To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China,and evaluate its feasibility and reliability.Methods The retrospective pelvic CT imaging data of 1 200 samples(600 males and 600 females)aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models.The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries.Using the ResNet34 model,500 samples of different sexes were randomly selected as training and verification set,the remaining samples were used as testing set.Initialization and transfer learning were used to train images that distinguish sex and left/right site.Mean absolute error(MAE)and root mean square error(RMSE)were used as primary indicators to evaluate the model.Results Prediction results varied between sexes,with bilateral models outperformed left/right unilateral ones,and transfer learning models showed superior performance over initial models.In the prediction results of bilateral transfer learning models,the male MAE was 7.74 years and RMSE was 9.73 years,the female MAE was 6.27 years and RMSE was 7.82 years,and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years.Conclusion The skeletal age estimation model,utilizing is-chial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning,can effectively estimate adult ischium age.关键词
法医人类学/年龄推断/深度学习/三维重建/骨盆/坐骨结节/迁移学习/汉族Key words
forensic anthropology/age estimation/deep learning/three-dimensional reconstruction/pel-vis/ischial tuberosity/transfer learning/Han population分类
医药卫生引用本文复制引用
张怀瀚,曹永杰,张吉,熊剪,马继伟,杨孝通,黄平,马永刚..CT三维重建技术结合深度学习算法推断成人坐骨年龄[J].法医学杂志,2024,40(2):154-163,10.基金项目
国家重点研发计划资助项目(2022YFC3302002) （2022YFC3302002）
上海市自然科学基金资助项目(23ZR1464400) （23ZR1464400）
