  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.
