C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation. (October 2022) - Chen, Gongping; Dai, Yu; Zhang, Jianxun
Highlights: First, we developed a novel cascaded convolutional neural network to segment the lesion from breast ultrasound images. Second, a bidirectional attention guidance network was designed to capture the context between global (low-level) and local (high-level) features. Third, we introduced a refinement residual network to obtain the more complete lesion mask. Moreover, the experimental results demonstrate that our method achieves the best overall performance on breast and renal ultrasound images segmentation. Abstract: 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 challenges 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 results. In addition, we apply the network on renal ultrasound images segmentation. In general, our method has good adaptability and robustness on ultrasound image segmentation.