X-ray images of the lower limb bone are the most commonly used imaging modality for clinical studies, and segmentation of the femur and tibia in an X-ray image is helpful for many medical studies such as diagnosis, surgery and treatment. In this paper, we propose a new approach based on pure dilated residual U-Net for the segmentation of the femur and tibia bones. The proposed approach employs dilated convolution completely to increase the receptive field, in this way, we can make full use of the advantages of dilated convolution. We conducted experiments and evaluations on datasets provided by Tianjin hospital. Comparison with the classical U-net and FusionNet, our method has fewer parameters, higher accuracy, and converges more rapidly, which means the high performance of the proposed method.

Leakage is an important factor affecting the safety of the dam. In the past, manual inspection is a significant way to monitor leakage risk. However, it is time-consuming, inefficient and difficult to quantitative evaluate such as the leakage area. A semantic segmentation method based on the fully convolutional network is proposed to replace the manual inspection for the dam leakage automatic detection. Thirty-eight high-resolution images of dam leakage are collected. FCN-8s and VGG16 backbone are adopted. The results indicated that the FCN-8s achieves the mIoU to 0.59 on the test set, which proves to be an efficient way to detect the dam leakage.

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