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.

中文翻译：

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基于带有空间变换器的深度残差网络的骨骼年龄预测。

目的

可以由医学专家手动评估手骨的X射线图像来进行骨龄预测。在实践中，工作量巨大，资源消耗大，测量时间长，容易受到人为因素的影响。因此，人工估计骨龄需要很长时间，并且结果会根据放射科医生的熟练程度而有很大的波动。

方法

左侧X射线图像数据被识别并进行了预处理。利用基于深度神经网络的X射线图像分析方法自动提取左关节骨龄的关键特征，并实现了模型的评估性能。

结果

在本文中，深度学习方法可用于获取X射线骨图像特征，而卷积神经网络可用于自动评估骨龄。与传统的图像分析技术相比，基于深度学习的特征区域提取方法可以提取性能更高的特征信息。基于深度学习算法中的残差网络（ResNet）模型，骨骼年龄评估模型检测到的骨骼年龄的平均绝对误差比传统方法和仅端到端深度学习方法好0.455。当学习率大于0.0005时，Inception Resnet v2模型的MAE高于大多数模型。骨龄预测的准确性高达97.6％。

结论

与传统的机器学习特征提取技术相比，基于特征提取的卷积神经网络在骨龄回归模型中具有更好的性能，并进一步提高了基于图像的骨龄评估的准确性。