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优化参考图谱发育指征的CHN智能骨龄评估方法研究

毛科技 武坤秀 陆伟 陈立建 毛家发

毛科技, 武坤秀, 陆伟, 陈立建, 毛家发. 优化参考图谱发育指征的CHN智能骨龄评估方法研究[J]. 电子与信息学报, 2023, 45(3): 958-967. doi: 10.11999/JEIT211577
引用本文: 毛科技, 武坤秀, 陆伟, 陈立建, 毛家发. 优化参考图谱发育指征的CHN智能骨龄评估方法研究[J]. 电子与信息学报, 2023, 45(3): 958-967. doi: 10.11999/JEIT211577
MAO Keji, WU Kunxiu, LU Wei, CHEN Lijian, MAO Jiafa. A Study of the CHN Intelligent Bone Age Assessment Method with Reference to Atlas Developmental Indications[J]. Journal of Electronics & Information Technology, 2023, 45(3): 958-967. doi: 10.11999/JEIT211577
Citation: MAO Keji, WU Kunxiu, LU Wei, CHEN Lijian, MAO Jiafa. A Study of the CHN Intelligent Bone Age Assessment Method with Reference to Atlas Developmental Indications[J]. Journal of Electronics & Information Technology, 2023, 45(3): 958-967. doi: 10.11999/JEIT211577

优化参考图谱发育指征的CHN智能骨龄评估方法研究

doi: 10.11999/JEIT211577
基金项目: 国家自然科学基金(62072410),浙江省基础公益研究计划(LGG22F020014)
详细信息
    作者简介:

    毛科技:男,副教授,研究方向为医学图像处理、智能计算、大数据分析

    武坤秀:女,硕士生,研究方向为医学图像处理

    陆伟:男,硕士生,研究方向为医学图像处理

    陈立建:男,博士生,研究方向为医学图像处理

    毛家发:男,教 授,研究方向为医学图像处理

    通讯作者:

    毛科技 maokeji@zjut.edu.cn

  • 中图分类号: TP391.41

A Study of the CHN Intelligent Bone Age Assessment Method with Reference to Atlas Developmental Indications

Funds: The National Natural Science Foundation of China(62072410), The Basic Public Welfare Research Project of Zhejiang Province (LGG22F020014)
  • 摘要: 骨龄(BA)是评估儿童生长发育是否正常的重要指标之一。中国人手腕骨发育标准-CHN计分法是目前中国儿童生长发育中骨龄评估(BAA)最常用的方法之一。但是在CHN计分法中,某些参照骨图谱的发育指征跨度较大,导致专家依据个人经验主观判断它的发育分期而影响评估准确度。在利用深度学习对该类图谱的发育分期进行评估时,会导致它的评估结果产生随机性。该文基于专家评估过的2万余张儿童手腕部X线片,在CHN计分法的基础上,在相邻发育分期间隔跨度较大的参照骨标准图谱之间勾绘新的成熟度指征,产生细化图谱,并利用层次分析法为其分配对应的成熟度得分,提高骨龄评价的准确率。该文在AlexNet网络的基础上融合Harris特征和卷积注意力模块,对各参照骨的发育分期进行评估。在自制的年龄分布为5-11岁的数据集上,采用优化后的CHN法得到的骨龄在容忍度为0.5岁和1岁时的准确率分别达到了94.6%和99.13%。实验结果表明所提方法可以更加精细地分辨儿童手腕骨发育程度,大幅提高骨龄评估的准确率,辅助临床应用。
  • 图  1  基于CHN法的骨龄智能评估系统结构图

    图  2  近节指骨Ⅴ介于3期与4期之间的图谱

    图  3  近节指骨Ⅴ的3期、3(5)期和4期对应的标准图谱

    图  4  ROI区域发育分期等级分类网络结构图

    图  5  不同方法下各ROI区域的发育分期等级分类准确率

    表  1  近节指骨Ⅴ各发育分期等级数量分布表

    发育分期等级多位专家意见是否
    全部一致
    图片数量在该分期图像中的
    占比(%)
    3一致43138.14
    不一致69961.86
    4一致83059
    不一致57741
    下载: 导出CSV

    表  2  CHN法近节指骨Ⅴ不同发育分期得分表

    12345678
    1924344557646771
    2129435365737780
    下载: 导出CSV

    表  3  近节指骨Ⅴ的成熟度等级以及发育指征和其对应成熟度得分

    发育分期Level3Level3(5)Level4
    对应标准图谱
    对应发育指征骨骺最大横径≥干骺端的一半骨骺近侧缘有凹起的趋势,开始出现致密白线骨骺近侧缘凹,明显致密
    对应得分(男)344045
    对应得分(女)434853
    下载: 导出CSV

    表  4  不同长度哈希码下的模型的准确率(%)

    None12243248128
    准确率94.3771.8398.3598.5997.1896.20
    下载: 导出CSV

    表  5  14块参照骨的发育分期分类准确率(%)

    mp3mp5pp5pp3pp1dp1dp3dp5CapitateHamateRadiusmc1mc3mc5
    准确率89.0096.7088.6086.0097.8785.3098.0296.4283.6693.1083.7297.4391.6092.38
    下载: 导出CSV

    表  6  不同年龄组在不同性能指标下的准确率(%)

    年龄(岁)Label1label2
    ±0.5岁±1岁±0.5岁±1岁
    5-686.8899.1786.5896.76
    6-785.7898.9584.0394.18
    7-886.3698.7383.4597.82
    8-980.7097.6981.3894.02
    9-1089.9298.3288.2497.70
    10-1188.7997.4388.4898.87
    下载: 导出CSV

    表  7  分别采用CHN法和Re_CHN法得到的骨龄评估准确率(%)

    标准BAA方法± 0.5岁±1岁
    label1Re_CHN87.9398.67
    CHN78.2797.54
    label2Re_CHN85.6296.20
    CHN72.7895.81
    label1 or label2Re_CHN94.6099.13
    CHN85.1898.50
    下载: 导出CSV

    表  8  不同方法在本文数据集下的性能指标(%)

    方法±0.5岁±1岁
    Regression CNN[14]69.6093.05
    Residual Attention Network[29]76.7391.40
    本文方法94.6099.13
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-27
  • 修回日期:  2022-07-26
  • 录用日期:  2022-08-02
  • 网络出版日期:  2022-08-04
  • 刊出日期:  2023-03-10

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