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[Dual-Channel Shoulder Joint X-ray Bone Age Estimation in Chinese Han Adolescents Based on the Fusion of Segmentation
Hui-Ming Zhou1,2, Dan-Yang Li2,3,4, Lei Wan2
1School of Forensic Medicine, Shanxi Medical University, Jinzhong 030600, Shanxi Province, China.
Objectives:
To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.
Methods:
A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to <18.0 years (708 males and 578 females) was conducted. Using random sampling, approximately 80% of the samples (1 032 cases) were selected as the training and validation sets for model learning, selection and optimization, and the other 20% samples (254 cases) were used as the test set to evaluate the model's generalization ability. The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels (manually annotated shoulder joint regions multiplied pixel-by-pixel with original images, followed by segmentation via the U-Net++ network to retain only key shoulder joint region information) were respectively input into four network models, namely VGG16, ResNet18, ResNet50 and DenseNet121 for bone age estimation. Additionally, manual bone age estimation was conducted on the test set data, and the results were compared with the four network models. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Pearson correlation coefficient (PCC) were used as main evaluation indicators.
Results:
In the test set, the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators. Among them, DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years, RMSE of 0.82 years, R2 of 0.76, and PCC (r) of 0.88. Manual estimation yielded an MAE of 0.82 years, ranking second only to dual-channel DenseNet121.
Conclusions:
The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results, and can effectively estimate the bone age of Chinese Han adolescents.
