{"title":"Performance comparison of deep learning methods on hand bone segmentation and bone age assessment","authors":"Yingying Lv, Jingtao Wang, Wenbo Wu, Yun Pan","doi":"10.1109/CoST57098.2022.00083","DOIUrl":null,"url":null,"abstract":"Bone age is the biological age that reflects the growth and development of human body. Bone age assessment has been applied and plays an important role in clinical medicine, sports science and justice. Reasonable convolution neural network (CNN) models can greatly improve the accuracy and efficiency of bone age assessment. By comparing various hand bone segmentation models trained by classical convolutional neural networks, we found that with intersection over inion (IoU) and dice similarity coefficient (Dice) as evaluation indexes, the segmentation model trained by U-Net had the best performance. Its IoU reached 0.9746, and its Dice reached 0.9871. This is contrary to our inherent recognition that the U-Net++ model is superior to the U-Net model. Based on the images segmented by U-Net, we applied five kinds of common convolutional neural networks to bone age prediction, with mean absolute error (MAE) and error accuracy within two years as evaluation indexes. The results showed that the MAE of Xception was 7.635 and the accuracy of errors within two years reached 97.59%. In this paper, we provide an optimal scheme for bone age image segmentation and bone age assessment, and provide a theoretical basis for the design of bone age assessment system.","PeriodicalId":135595,"journal":{"name":"2022 International Conference on Culture-Oriented Science and Technology (CoST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Culture-Oriented Science and Technology (CoST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoST57098.2022.00083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Bone age is the biological age that reflects the growth and development of human body. Bone age assessment has been applied and plays an important role in clinical medicine, sports science and justice. Reasonable convolution neural network (CNN) models can greatly improve the accuracy and efficiency of bone age assessment. By comparing various hand bone segmentation models trained by classical convolutional neural networks, we found that with intersection over inion (IoU) and dice similarity coefficient (Dice) as evaluation indexes, the segmentation model trained by U-Net had the best performance. Its IoU reached 0.9746, and its Dice reached 0.9871. This is contrary to our inherent recognition that the U-Net++ model is superior to the U-Net model. Based on the images segmented by U-Net, we applied five kinds of common convolutional neural networks to bone age prediction, with mean absolute error (MAE) and error accuracy within two years as evaluation indexes. The results showed that the MAE of Xception was 7.635 and the accuracy of errors within two years reached 97.59%. In this paper, we provide an optimal scheme for bone age image segmentation and bone age assessment, and provide a theoretical basis for the design of bone age assessment system.
骨龄是反映人体生长发育的生物年龄。骨龄评估在临床医学、体育科学和司法等领域都有广泛的应用和作用。合理的卷积神经网络(CNN)模型可以大大提高骨龄评估的准确性和效率。通过对比经典卷积神经网络训练的各种手骨分割模型,我们发现以交叉数(intersection over inion, IoU)和骰子相似系数(dice, dice)作为评价指标,U-Net训练的手骨分割模型表现最好。IoU为0.9746,Dice为0.9871。这与我们固有的认识相反,即unet++模型优于U-Net模型。在U-Net分割图像的基础上,应用5种常用卷积神经网络进行骨龄预测,以平均绝对误差(MAE)和2年内的误差精度为评价指标。结果表明:异常的MAE为7.635,2年内误差的准确率达到97.59%。本文提出了一种骨年龄图像分割和骨年龄评估的优化方案,为骨年龄评估系统的设计提供了理论依据。