Breast lesion segmentation from breast ultrasound (BUS) videos could assist
in early diagnosis and treatment. Existing video object segmentation (VOS)
methods usually require dense annotation, which is often inaccessible for
medical datasets. Furthermore, they suffer from accumulative errors and a lack
of explicit space-time awareness. In this work, we propose a novel two-shot
training paradigm for BUS video segmentation. It not only is able to capture
free-range space-time consistency but also utilizes a source-dependent
augmentation scheme. This label-efficient learning framework is validated on a
challenging in-house BUS video dataset. Results showed that it gained
comparable performance to the fully annotated ones given only 1.9% training
labels.

该研究提出了一种新颖的两阶段训练范式，用于乳腺超声视频的分割，通过多样化的数据增强策略实现了自由空时一致性的捕捉，并在医学数据集上进行了验证，结果表明该方法在仅使用 1.9％的训练标签时能够获得与完全注释数据相当的性能。

两次拍摄足够吗？一种用于乳腺超声视频分割的标签高效方法

Is Two-shot All You Need? A Label-efficient Approach for Video  Segmentation in Breast Ultrasound

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 this https URL

通过精心策划的一个覆盖广泛的视频数据集和创新的 FLA-Net 模型，实现了超声视频乳腺病变分割的最先进性能，并显著减少了时间和空间复杂度。