  Breast tumor segmentation is a critical task in computer-aided diagnosis
(CAD) systems for breast cancer detection because accurate tumor size, shape
and location are important for further tumor quantification and classification.
However, segmenting small tumors in ultrasound images is challenging, due to
the speckle noise, varying tumor shapes and sizes among patients, and the
existence of tumor-like image regions. Recently, deep learning-based approaches
have achieved great success for biomedical image analysis, but current
state-of-the-art approaches achieve poor performance for segmenting small
breast tumors. In this paper, we propose a novel deep neural network
architecture, namely Enhanced Small Tumor-Aware Network (ESTAN), to accurately
and robustly segment breast tumors. ESTAN introduces two encoders to extract
and fuse image context information at different scales and utilizes
row-column-wise kernels in the encoder to adapt to breast anatomy. We validate
the proposed approach and compare it to nine state-of-the-art approaches on
three public breast ultrasound datasets using seven quantitative metrics. The
results demonstrate that the proposed approach achieves the best overall
performance and outperforms all other approaches on small tumor segmentation.
