  Automatic breast lesion segmentation in ultrasound helps to diagnose breast
cancer, which is one of the dreadful diseases that affect women globally.
Segmenting breast regions accurately from ultrasound image is a challenging
task due to the inherent speckle artifacts, blurry breast lesion boundaries,
and inhomogeneous intensity distributions inside the breast lesion regions.
Recently, convolutional neural networks (CNNs) have demonstrated remarkable
results in medical image segmentation tasks. However, the convolutional
operations in a CNN often focus on local regions, which suffer from limited
capabilities in capturing long-range dependencies of the input ultrasound
image, resulting in degraded breast lesion segmentation accuracy. In this
paper, we develop a deep convolutional neural network equipped with a global
guidance block (GGB) and breast lesion boundary detection (BD) modules for
boosting the breast ultrasound lesion segmentation. The GGB utilizes the
multi-layer integrated feature map as a guidance information to learn the
long-range non-local dependencies from both spatial and channel domains. The BD
modules learn additional breast lesion boundary map to enhance the boundary
quality of a segmentation result refinement. Experimental results on a public
dataset and a collected dataset show that our network outperforms other medical
image segmentation methods and the recent semantic segmentation methods on
breast ultrasound lesion segmentation. Moreover, we also show the application
of our network on the ultrasound prostate segmentation, in which our method
better identifies prostate regions than state-of-the-art networks.
