Breast cancer investigation is of great significance and developing tumor detection methodologies is a critical need. However, it is a challenging task for breast cancer detection using breast ultrasound (BUS) images due to the complicated breast structure and poor quality of the images. In this paper, we propose a novel tumor saliency estimation (TSE) model guided by enriched breast anatomy knowledge to localize the tumor. First, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with increasing 10% of F_meansure on the public BUS dataset.

Deep learning approaches have achieved impressive results in breast ultrasound (BUS) image segmentation. However, these methods did not solve uncertainty and noise in BUS images well. To address this issue, we present a novel deep learning structure for BUS image semantic segmentation by analyzing the uncertainty using a pyramid fuzzy block and generating a novel feature based on connectedness. Firstly, feature maps in the proposed network are down-sampled to different resolutions. Fuzzy transformation and uncertainty representation are applied to each resolution to obtain the uncertainty degree on different scales. Meanwhile, the BUS images contain layer structures. From top to bottom, there are skin layer, fat layer, mammary layer, muscle layer, and background area. A spatial recurrent neural network (RNN) is utilized to calculate the connectedness between each pixel and the pixels on the four boundaries in horizontal and vertical lines. The spatial-wise context feature can introduce the characteristic of layer structure to deep neural network. Finally, the original convolutional features are combined with connectedness feature according to the uncertainty degrees. The proposed methods are applied to two datasets: a BUS image benchmark with two categories (background and tumor) and a five-category BUS image dataset with fat layer, mammary layer, muscle layer, background, and tumor. The proposed method achieves the best results on both datasets compared with eight state-of-the-art deep learning-based approaches.