C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation

Background and objective: Breast lesions segmentation is an important step of computer-aided diagnosis system. However, speckle noise, heterogeneous structure, and similar intensity distributions bring chal-lenges for breast lesion segmentation.Methods: In this paper, we presented a novel cascaded convolutional neural network integrating U-net, bidirectional attention guidance network (BAGNet) and refinement residual network (RFNet) for the lesion segmentation in breast ultrasound images. Specifically, we first use U-net to generate a set of saliency maps containing low-level and high-level image structures. Then, the bidirectional attention guidance network is used to capture the context between global (low-level) and local (high-level) features from the saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue on the lesion regions. Furthermore, we developed a refinement residual network based on the core architecture of U-net to learn the difference between rough saliency feature maps and ground-truth masks. The learning of residuals can assist us to obtain a more complete lesion mask.Results: To evaluate the segmentation performance of the network, we compared with several state-of-the-art segmentation methods on the public breast ultrasound dataset (BUSIS) using six commonly used evaluation metrics. Our method achieves the highest scores on six metrics. Furthermore, p-values indicate significant differences between our method and the comparative methods.Conclusions: Experimental results show that our method achieves the most competitive segmentation re-sults. In addition, we apply the network on renal ultrasound images segmentation. In general, our method has good adaptability and robustness on ultrasound image segmentation.(c) 2022 Elsevier B.V. All rights reserved.