  Ultrasound video-based breast lesion segmentation provides a valuable
assistance in early breast lesion detection and treatment. However, existing
works mainly focus on lesion segmentation based on ultrasound breast images
which usually can not be adapted well to obtain desirable results on ultrasound
videos. The main challenge for ultrasound video-based breast lesion
segmentation is how to exploit the lesion cues of both intra-frame and
inter-frame simultaneously. To address this problem, we propose a novel
Spatial-Temporal Progressive Fusion Network (STPFNet) for video based breast
lesion segmentation problem. The main aspects of the proposed STPFNet are
threefold. First, we propose to adopt a unified network architecture to capture
both spatial dependences within each ultrasound frame and temporal correlations
between different frames together for ultrasound data representation. Second,
we propose a new fusion module, termed Multi-Scale Feature Fusion (MSFF), to
fuse spatial and temporal cues together for lesion detection. MSFF can help to
determine the boundary contour of lesion region to overcome the issue of lesion
boundary blurring. Third, we propose to exploit the segmentation result of
previous frame as the prior knowledge to suppress the noisy background and
learn more robust representation. In particular, we introduce a new publicly
available ultrasound video breast lesion segmentation dataset, termed UVBLS200,
which is specifically dedicated to breast lesion segmentation. It contains 200
videos, including 80 videos of benign lesions and 120 videos of malignant
lesions. Experiments on the proposed dataset demonstrate that the proposed
STPFNet achieves better breast lesion detection performance than
state-of-the-art methods.
