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Segmenting reference bones from radiographs of the hand is important for bone age assessment. Due to the influence of the irregular shapes and the adjacent positions of the wrist reference bones, it is difficult for the expert to accurately estimate the mature indication of the wrist reference bones in the figures. How to precisely segment the reference bones automatically from the radiographs is a challenge. For this is problem, an improved U-Net, Attention Residual U-Net (ARU-Net) proposed in this paper. Firstly, we extract the reference bone region of interest (ROI) by faster region-based convolutional neural networks(R-CNN). Then, the pre-processed ROI is fed into ARU-Net for segmentation. On the basis of traditional U-Net, ARU-Net adds residual mapping and attention mechanism, which improves the utilization rate of features and the accuracy of reference bone segmentation. Finally, a post-processing method including the flood fill algorithm and the morphological operation is used to eliminate jagged edges and holes in the segmented result. The hamate is one of the most difficult reference bones to segment in the wrist. This paper takes it as an example to assess the performance of ARU-Net. Experiments show that compared with Fully Convolutional Neural Network (FCN), U-Net and ResUnet, the accuracy and F1 scores of ARU-Net are higher. Its accuracy rate is 96.41%, and F1 score is 0.9529. The post-processing method can further improve the result. Finally, the accuracy rate reaches 96.51%, and the F1 score reaches 0.9544. ARU-Net can precisely segment the reference bone, which facilitates the expert to assess its mature indication, so as to accurately evaluate the bone age.

ARU-Net: Research and Application for Wrist Reference Bone Segmentation