  The medical datasets are usually faced with the problem of scarcity and data
imbalance. Moreover, annotating large datasets for semantic segmentation of
medical lesions is domain-knowledge and time-consuming. In this paper, we
propose a new object-blend method(short in soft-CP) that combines the
Copy-Paste augmentation method for semantic segmentation of medical lesions
offline, ensuring the correct edge information around the lession to solve the
issue above-mentioned. We proved the method's validity with several datasets in
different imaging modalities. In our experiments on the KiTS19[2] dataset,
Soft-CP outperforms existing medical lesions synthesis approaches. The Soft-CP
augementation provides gains of +26.5% DSC in the low data regime(10% of data)
and +10.2% DSC in the high data regime(all of data), In offline training data,
the ratio of real images to synthetic images is 3:1.
