  Deep learning has achieved remarkable success in medicalimage segmentation,
but it usually requires a large numberof images labeled with fine-grained
segmentation masks, andthe annotation of these masks can be very expensive
andtime-consuming. Therefore, recent methods try to use un-supervised domain
adaptation (UDA) methods to borrow in-formation from labeled data from other
datasets (source do-mains) to a new dataset (target domain). However, due tothe
absence of labels in the target domain, the performance ofUDA methods is much
worse than that of the fully supervisedmethod. In this paper, we propose a
weakly supervised do-main adaptation setting, in which we can partially label
newdatasets with bounding boxes, which are easier and cheaperto obtain than
segmentation masks. Accordingly, we proposea new weakly-supervised domain
adaptation method calledBox-Adapt, which fully explores the fine-grained
segmenta-tion mask in the source domain and the weak bounding boxin the target
domain. Our Box-Adapt is a two-stage methodthat first performs joint training
on the source and target do-mains, and then conducts self-training with the
pseudo-labelsof the target domain. We demonstrate the effectiveness of
ourmethod in the liver segmentation task. Weakly supervised do-main adaptation
