Skeletal bone age prediction based on a deep residual network with spatial transformer. (December 2020)

• Record Type: Journal Article
• Authors: Han, Yaxin; Wang, Guangbin
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: X-ray bone image -- Image processing -- Deep learning -- Automatic bone age prediction -- Convolutional neural network; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105754
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Clinicians predict bone age by manually reading X-ray hand bone images. SVM automatic bone age assessment method has bone age prediction capability. ST-ResNet network model is based on ResNet and spatial transformer. ST-ResNet network model demonstrates better bone age prediction accuracy. Deep learning can facilitate bone age detection more effectively. Abstract: Objective: Bone age prediction can be performed by medical experts manually assessment of X-ray images of the hand bone. In practice, the workload is huge, resource consumption is large, measurement takes a long time, and it is easily influenced by human factors. As such, manual estimation of bone age takes a long time and the results fluctuate greatly depending on the proficiency of the radiologist. Methods: The left-hand X-ray image data was identified and pre-processed. X-ray image analysis method using on deep neural network was used to automatically extract the key features of the left-hand joint bone age, and evaluation performance of the model was implemented. Results: In this paper, the deep learning method can be used to obtain the X-ray bone image features, and the convolutional neural network is used to automatically assess the age of bone. The feature region extraction method based on deep learning can extract feature information with superior performance compared to the traditional image analysis technique. Based on the residual network (ResNet) model in the deep learning algorithm, the average absolute error of the age of bones detected by the bone age assessment model is 0.455 better than traditional methods and only end-to-end deep learning methods. When the learning rate is greater than 0.0005, the MAE of Inception Resnet v2 model is higher than most models. Accuracy of bone age prediction is as high as 97.6%. Conclusion: In comparison with the traditional machine learning feature extraction technique, the convolutional neural network based on feature extraction has better performance in the bone age regression model, and further improves the accuracy of image-based age of bone assessment.