Bone Age Assessment Based on Two-Stage Deep Neural Networks

Abstract

Skeletal bone age assessment is a clinical practice to diagnose the maturity of children. To accurately assess the bone age, we proposed an automatic bone age assessment method in this paper based on deep convolution network. This method includes two stages: mask generation network and age assessment network. A U-Net convolution network with pretrained VGG16 as the encoder is used to extract the mask of bones. For the assessment module, the original images are fused together with the generated mask image to obtain segmented normalized hand bone images. We then built a multiple output convolution network for accurate age assessment. Finally, the bone age regression problem is transformed into the K-1 binary classification sub-problems. Our model was tested on RSNA2017 Pediatric Bone Age dataset. We were able to achieve the mean absolute error (MAE) of 5.98 months, which outperforms other common methods for bone age assessment. The proposed method could be used for developing fully automatic bone age assessment with better accuracy.