优化参考图谱发育指征的CHN智能骨龄评估方法研究

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

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。

Bone Age(BA)is one of the most important indicators in evaluating children's growth.The Bone Age Assessment(BAA)based on Chinese wrist bone development standard-CHN(CHN)scoring method is widely used in the evaluation of children's growth and development and height prediction.However,the adjacent developmental levels of some reference bones last longer,leading to the subjective judgment of developmental levels by experts based on personal experience,which affects the accuracy of predictions.When deep learning is used to evaluate the developmental levels of these atlases,the prediction results will be random.In this paper,based on more than 20000 X-ray images evaluated by experts,a new mature indicator with a large interval with a large interval is drawn to generate exquisite atlas to perform some reference bones.Additionally,the corresponding maturity score is determined by analyzing the level structure process to maximize the impact of error-level prediction on BAA.Combining Harris features and convolutional blocks of the convolutional neural network of the attention module is designed to evaluate automatically the level of bone maturity.In addition,an annotated database with an age distribution of 5-11 years is built to train and evaluate the method.The accuracy of predictions obtained by adding a new standard atlas to the CHN method reaches 94.6%and 99.13%when the tolerance is 0.5 years and 1 year,respectively.The experimental results show that the method proposed in this paper can distinguish the development degree of reference bones more precisely,and improve greatly the accuracy of BAA,proving the potential for practical clinical application.