摘要
儿童和青少年的骨龄表明了他们的生长发育情况。传统的骨龄评估方法是医生通过肉眼观察全左手的X光片中多块特定骨头的成熟程度,其精确性很依赖医生的主观判断能力,且评估较为费时。目前已经有基于全手掌骨图像且利用深度卷积神经网络进行骨龄自动评估的方法。为了提高骨龄识别的精度,文中提出从每个全手掌骨中分割出用于骨龄评估的14块特定骨头,然后对每块骨头训练出AlexNet卷积神经网络模型以进行骨成熟等级评估。另外,考虑到骨头发育是个连续过程,不同于传统的骨成熟等级判定,利用网络所输出的两个最可能等级的分类概率来计算骨头的加权得分。测试结果表明,该方法的平均骨龄误差为0.456岁,误差在1.0岁以内的准确率达到94.64%,显著优于基于全手掌骨图像的骨龄自动评估方法。
The bone-ages of children and adolescents indicate their growth condition.Traditional clinical method of bone-age assessment is to observe the bone maturity of multiple particular bones inside the X-ray film of the whole left hand by the doctor’s eyes.The assessment accuracy greatly depends on the doctor’s subjective judgment ability,and the evaluation is time-consuming.At present,deep convolution neural network has been used for automated bone-age assessment based on the whole bone image of left hand.In order to improve the accuracy of bone-age assessment,this paper proposed to segment 14 specific bones used for bone-age assessment from each whole hand bone image,and then train a deep convolution neural network(AlexNet)for each one of 14 specific bones to evaluate the bone maturity level.In addition,considering that bone development is a continuous process,unlike selecting some discrete growth-level of bone in the traditional method,this paper uses the classification probabilities of the two most probable levels outputted by the automated neural network to calculate the weighted score.The test results show that the proposed method has the average bone-age error of 0.456 year and has an accuracy of 94.64%when the allowed error range which 1.0 year,which is significantly better than the automated bone-age assessment method based on the whole hand image.
作者
池凯凯
蔡荣辉
丁维龙
宦若虹
毛科技
CHI Kai-kai;CAI Rong-hui;DING Wei-long;HUAN Ruo-hong;MAO Ke-ji(School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《计算机科学》
CSCD
北大核心
2019年第S11期89-93,共5页
Computer Science
基金
国家自然科学基金(61872322)
浙江省重点研发计划项目(2018C01082)资助
关键词
骨龄评估
深度学习
卷积神经网络
准确率
Bone-age assessment
Deep learning
Convolutional neural network
Accuracy rate
