  Breast lesion segmentation in ultrasound (US) videos is essential for
diagnosing and treating axillary lymph node metastasis. However, the lack of a
well-established and large-scale ultrasound video dataset with high-quality
annotations has posed a persistent challenge for the research community. To
overcome this issue, we meticulously curated a US video breast lesion
segmentation dataset comprising 572 videos and 34,300 annotated frames,
covering a wide range of realistic clinical scenarios. Furthermore, we propose
a novel frequency and localization feature aggregation network (FLA-Net) that
learns temporal features from the frequency domain and predicts additional
lesion location positions to assist with breast lesion segmentation. We also
devise a localization-based contrastive loss to reduce the lesion location
distance between neighboring video frames within the same video and enlarge the
location distances between frames from different ultrasound videos. Our
experiments on our annotated dataset and two public video polyp segmentation
datasets demonstrate that our proposed FLA-Net achieves state-of-the-art
performance in breast lesion segmentation in US videos and video polyp
segmentation while significantly reducing time and space complexity. Our model
and dataset are available at https://github.com/jhl-Det/FLA-Net.
