Merged U-Net for Bone Tumors X-Ray Images Segmentation

Bone tumors X-ray image segmentation is crucial for many medical image processing applications such as X-ray image enhancement, lesion diagnosis, etc. In this paper, we propose a new topological structure of u-net, named merged u-net, for bone tumors X-ray image segmentation. We attach a top-down merged branch to the vanilla encoder-decoder network, enhancing the hierarchical feature aggregation. In the merged path, a multi-feature aggregation block, named merged gate, is proposed for better localization of lesion area. According to the attention matrix, the proposed merged gate can generate a reconstructed feature that contains both low-level information and high-level information. Then we incorporate the reconstructed feature with the small scare feature cued with a local feature fusion method, achieving multi-scare feature aggregation. Additionally, we propose a new X-ray image dataset for bone tumors segmentation, which consists of 88 benign images and 217 malignant images, provided with corresponding segmentation masks. Experimental results demonstrate that the proposed merged u-net outperforms other u-net based medical segmentation methods on the proposed X-ray image dataset.