Abstract
The research on making accurate segmentation of images in ultrasound inspecting is a challenging in the medical image segmentation domain. It is tough to obtain a satisfactory segmentation of U-Net networks in deep learning. The difficulties are contributed to low contrast between detected targets and surrounding tissues, the large differences between target edges and shapes, and so forth. Based on batch-free normalization (BFN) and a residual attention block, a class of Attention Res BFN U-Net (ARB U-Net) network with a deep encoder and a shallow decoder is proposed, and the depth and the performance of the network is improved. With utilizing Dice loss and BCE loss are utilized as segmentation loss and classification loss respectively, a kind of Dice-BCE loss function is constructed on the basis of multi-task weighting strategy. 450 ultrasound images were used as the training set and another 50 images were used as the test set. The average segmentation accuracy of the test data set reached 97.1%, which is about 3% better than that of the traditional U-Net and its common variants. The experimental results show that the proposed network can significantly improve the accuracy and precision of ultrasound image segmentation of suprapatellar bursa.
Published Version
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