[1]谷静,马瑞齐,朱恒安.基于卷积神经网络的X图像骨龄评估方法[J].中国医学物理学杂志,2022,39(3):305-310.[doi:DOI:10.3969/j.issn.1005-202X.2022.03.008]
　GU Jing,MA Ruiqi,ZHU Hengan.Convolutional neural network-based method for bone age assessment in X-ray image[J].Chinese Journal of Medical Physics,2022,39(3):305-310.[doi:DOI:10.3969/j.issn.1005-202X.2022.03.008]
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基于卷积神经网络的X图像骨龄评估方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第3期
页码:
305-310
栏目:
医学影像物理
出版日期:
2022-03-28

文章信息/Info

Title:
Convolutional neural network-based method for bone age assessment in X-ray image
文章编号:
1005-202X（2022）03-0305-06
作者:
谷静; 马瑞齐; 朱恒安
西安邮电大学电子工程学院， 陕西 西安 710121
Author(s):
GU Jing;  MA Ruiqi;  ZHU Hengan
School of Electronic Engineering, Xian University of Posts and Telecommunications, Xian 710121, China
关键词:
骨龄评估; 卷积神经网络; 精减评估区域; 等级计分法; TW3-C
Keywords:
Keywords: bone age assessment convolutional neural network refining the assessment area grade scoring method TW3-C
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2022.03.008
文献标志码:
A
摘要:
针对传统方法中骨骺评估区域多、评估结果对医生的依赖性强、评估准确率低等问题，在TW3-C法基础上提出一种改进的骨龄评估方法。根据中国儿童骨骼发育特点，利用卷积神经网络对评估区域进行精减和分类，将传统的13个骨骼评估区域精减至10个，并改进等级计分法。试验结果显示，在1岁误差范围内，该方法将骨龄的预测值准确率提升至男性94.42%、女性93.64%，平均绝对误差为男性0.414 3岁、女性0.428 6岁，与典型的骨龄评估方法相比，准确率得到显著提高。
Abstract:
Abstract: To solve the problems in traditional methods such as lots of assessment areas in the epiphysis, high dependence of assessment results on doctors and low assessment accuracy, an improved bone age assessment method based on TW3-C method is proposed. According to the characteristics of Chinese childrens bone development, convolutional neural network is used to refine and classify the assessment areas, reducing the traditional 13 bone assessment areas to 10, and the grade scoring method is also improved. The experimental results show that within the error range of 1-year-old, the proposed method improves the accuracy of the predicted value of bone age to 94.42% for men and 93.64% for women. The average absolute error is 0.414 3 years old for men and 0.428 6 years old for women. Compared with that of typical bone age assessment method, the accuracy of the proposed method for bone age assessment is significantly improved.

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备注/Memo

备注/Memo:
【收稿日期】2021-07-16 【基金项目】陕西省自然科学基础研究计划资助项目（2020SF-370）；西安邮电大学研究生创新基金项目（CXJJLY202029） 【作者简介】谷静，副教授，主要从事通信与信息系统、图像处理研究，E-mail: guj@xupt.edu.cn 【通信作者】马瑞齐，研究生，主要从事深度学习医疗X图像处理研究，E-mail: mrqlove999@163.com
更新日期/Last Update: 2022-03-28