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。