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Tytuł:
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[Comparison of Three CNN Models Applied in Bone Age Assessment of Pelvic Radiographs of Adolescents].
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Autorzy:
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Peng LQ; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun-Yat Sen University, Guangzhou 510080, China.; Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Wan L; Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Wang MW; Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Li Z; Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Wang P; Shanghai Zhenpu Information Technology Co. Ltd., Shanghai 200444, China.
Liu TA; Shanghai Zhenpu Information Technology Co. Ltd., Shanghai 200444, China.
Wang YH; Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
Zhao H; Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun-Yat Sen University, Guangzhou 510080, China.
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Źródło:
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Fa yi xue za zhi [Fa Yi Xue Za Zhi] 2020 Oct; Vol. 36 (5), pp. 622-630.
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Typ publikacji:
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Journal Article
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Język:
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Chinese
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Imprint Name(s):
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Publication: Shanghai : Si fa bu si fa jian ding ke xue yan jiu yuan
Original Publication: Shanghai : Si fa bu si fa jian ding ke xue ji shu yan jiu suo,
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MeSH Terms:
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Age Determination by Skeleton*
Pelvis*
Adolescent ; Adult ; Child ; China ; Female ; Humans ; Male ; Radiography ; Young Adult
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Contributed Indexing:
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Keywords: forensic anthropology; age determination by skeleton; pelvis; image recognition; deep learning; convolutional neural networks; Han nationality; adolescents
Local Abstract: [Publisher, Chinese] 运用3种卷积神经网络模型对青少年骨盆骨龄评估的比较. [Publisher, Chinese] 目的 比较VGG19、Inception-V3、Inception-ResNet-V2 3种深度学习（deep learning，DL）模型基于骨盆X线片图像进行骨龄自动评估的性能。 方法 采集我国5省市11.0~<21.0周岁汉族青少年骨盆X线片图像962例（男性481例，女性481例），将上述图像进行预处理作为研究对象。采用随机抽样的方法抽取80%作为训练集、验证集，用于模型拟合和超参数的调整。20%作为测试集，用于评估模型泛化的能力。通过比较模型估计值与生活年龄的均方根误差（root mean square error，RMSE）、平均绝对误差（mean absolute error，MAE）以及绘制Bland-Altman散点图来评估3种模型的性能。 结果 VGG19模型预测年龄与生活年龄的平均RMSE、MAE分别为1.29、1.02岁，Inception-V3模型预测年龄与生活年龄的平均RMSE、MAE分别为1.17、0.82岁，Inception-ResNet-V2模型预测年龄与生活年龄的平均RMSE、MAE分别为1.11、0.84岁。Bland-Altman散点图显示Inception-ResNet-V2模型的差值的均值最小。 结论 在对青少年骨盆的自动骨龄评估中，Inception-ResNet-V2模型性能最优，Inception-V3模型与VGG19模型性能相当。. [Publisher, Chinese] 法医人类学；年龄测定，骨骼；骨盆；图像识别；深度学习；卷积神经网络；汉族；青少年.
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Entry Date(s):
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Date Created: 20201209 Date Completed: 20201210 Latest Revision: 20220418
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Update Code:
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20240105
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DOI:
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10.12116/j.issn.1004-5619.2020.05.004
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PMID:
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33295161
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Abstract: Objective To compare the performance of three deep-learning models （VGG19, Inception-V3 and Inception-ResNet-V2） in automatic bone age assessment based on pelvic X-ray radiographs. Methods A total of 962 pelvic X ray radiographs taken from adolescents （481 males, 481 females） aged from 11.0 to 21.0 years in five provinces and cities of China were collected, preprocessed and used as objects of study. Eighty percent of these X ray radiographs were divided into training set and validation set with random sampling method and used for model fitting and hyper-parameters adjustment. Twenty percent were used as test sets, to evaluate the ability of model generalization. The performances of the three models were assessed by comparing the root mean square error （RMSE）, mean absolute error （MAE） and Bland-Altman plots between the model estimates and the chronological ages. Results The mean RMSE and MAE between bone age estimates of the VGG19 model and the chronological ages were 1.29 and 1.02 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-V3 model and the chronological ages were 1.17 and 0.82 years, respectively. The mean RMSE and MAE between bone age estimates of the Inception-ResNet-V2 model and the chronological ages were 1.11 and 0.84 years, respectively. The Bland-Altman plots showed that the mean value of differences between bone age estimates of Inception-ResNet-V2 model and the chronological ages was the lowest. Conclusion In the automatic bone age assessment of adolescent pelvis, the Inception-ResNet-V2 model performs the best while the Inception-V3 model achieves a similar accuracy as VGG19 model.
Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose.
(Copyright© by the Editorial Department of Journal of Forensic Medicine.)
