  Breast ultrasound (BUS) image segmentation plays a crucial role in a
computer-aided diagnosis system, which is regarded as a useful tool to help
increase the accuracy of breast cancer diagnosis. Recently, many deep learning
methods have been developed for segmentation of BUS image and show some
advantages compared with conventional region-, model-, and traditional
learning-based methods. However, previous deep learning methods typically use
skip-connection to concatenate the encoder and decoder, which might not make
full fusion of coarse-to-fine features from encoder and decoder. Since the
structure and edge of lesion in BUS image are common blurred, these would make
it difficult to learn the discriminant information of structure and edge, and
reduce the performance. To this end, we propose and evaluate a coarse-to-fine
fusion convolutional network (CF2-Net) based on a novel feature integration
strategy (forming an 'E'-like type) for BUS image segmentation. To enhance
contour and provide structural information, we concatenate a super-pixel image
and the original image as the input of CF2-Net. Meanwhile, to highlight the
differences in the lesion regions with variable sizes and relieve the imbalance
issue, we further design a weighted-balanced loss function to train the CF2-Net
effectively. The proposed CF2-Net was evaluated on an open dataset by using
four-fold cross validation. The results of the experiment demonstrate that the
CF2-Net obtains state-of-the-art performance when compared with other deep
learning-based methods
