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.
@article{arxiv.2203.10507,
title = {Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions},
author = {Pingping Dai and Licong Dong and Ruihan Zhang and Haiming Zhu and Jie Wu and Kehong Yuan},
journal= {arXiv preprint arXiv:2203.10507},
year = {2022}
}