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法医学杂志 2018, Vol. 34 Issue (1) :27-32    DOI: 10.3969/j.issn.1004-5619.2018.01.006
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基于深度学习实现维吾尔族青少年左手腕关节骨龄自动化评估
胡婷鸿1,2,火  忠3,刘太昂4,王  飞3,万  雷1,汪茂文1,陈  腾2,王亚辉1
1. 司法鉴定科学研究院 上海市法医学重点实验室 上海市司法鉴定专业技术服务平台,上海 200063; 2. 西安交通大学医学部法医学院,陕西 西安 710061; 3. 新疆维吾尔自治区人民医院,新疆 乌鲁木齐 830000; 4. 上海帆阳信息科技有限公司,上海 200444
Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning
HU Ting-hong1,2, HUO Zhong3, LIU Tai-ang4, WANG Fei3, WAN Lei1, WANG Mao-wen1, CHEN Teng2, WANG Ya-hui1
1. Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China; 2. Department of Forensic Science, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China; 3. People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830000, China; 4. Shanghai Fanyang Information Technology Co., LTD., Shanghai 200444, China

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摘要 目的 将深度学习运用于维吾尔族青少年左手腕关节数字化X线摄影(digital radiography,DR)图像识别中,实现骨龄评估的自动化,探索该方法在法医骨龄鉴定中的应用价值。 方法 在我国新疆维吾尔自治区采集13.0~19.0岁维吾尔族男性青少年245例、女性青少年227例左手腕关节DR图像,将预处理后的图像作为研究对象,将AlexNet作为图像识别的回归模型。在上述总样本中分别选取男、女性60%左手腕关节DR图像样本作为网络训练集,10%的样本作为验证集,余30%作为测试集,获取与样本真实年龄误差范围分别在±1.0岁、±0.7岁以内的图像识别准确率。 结果 深度学习的内测结果:误差范围在±1.0岁及 ±0.7岁以内的网络训练集准确率,男性分别为81.4%和75.6%,女性分别为80.5%和74.8%。误差范围在±1.0岁及±0.7岁以内的测试集准确率,男性分别为79.5%和71.2%,女性分别为79.4%和66.2%。 结论 青少年左手腕关节骨龄研究与深度学习相结合,具有较高的准确性及较好的可行性,为躯体其余骨关节的骨龄自动化评估体系奠定研究基础。
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胡婷鸿
火忠
刘太昂
关键词法医人类学;年龄测定   骨骼;腕关节;体层摄影术   X线;图像识别;深度学习;维吾尔族;青少年     
Abstract: Objective To realize the automated bone age assessment by applying deep learning to digital radiography (DR) image recognition of left wrist joint in Uyghur teenagers, and explore its practical application value in forensic medicine bone age assessment. Methods The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60% of male and female DR images of left wrist joint were selected as net train set, and 10% of samples were selected as validation set. As test set, the rest 30% were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. Results The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. Conclusion The combination of bone age research on teenagers’ left wrist joint and deep learning, which has high accuracy and good feasibility, can be the research basis of bone age automatic assessment system for the rest joints of body.
Keywordsforensic anthropology   age determination by skeleton   carpal joints   tomography, X-ray   image recognition   deep learning   Uyghur nationality   adolescent     
Fund:

国家自然科学基金资助项目(81571859,81102305, 81401559);上海市法医学重点实验室资助项目(17DZ2273200);上海市司法鉴定专业技术服务平台资助项目(16DZ2290900);上海市法医学重点实验室开放基金资助项目(KF1706)

Corresponding Authors: 陈腾,男,博士,教授,博导,主要从事毒品依赖的神经生物学机制,人类基因组多态性,法医临床学研究;E-mail:chenteng@mail.xjtu.edu.cn 王亚辉,男,副研究员,主要从事法医临床学研究;E-mail:wangyh@ssfjd.cn   
About author: 胡婷鸿(1993—),女,硕士研究生,主要从事法医临床学研究;E-mail:1072977906@qq.com
引用本文:   
胡婷鸿, 火忠, 刘太昂,等.基于深度学习实现维吾尔族青少年左手腕关节骨龄自动化评估[J]  法医学杂志, 2018,34(1): 27-32
HU Ting-Hong, HUO Zhong, LIU Tai-Ang,et al..Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning[J]  , 2018,34(1): 27-32
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