Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions
This addresses data scarcity for medical image segmentation, though it appears incremental as an enhancement to existing Copy-Paste methods.
The paper tackles the problem of scarce and imbalanced medical datasets for lesion segmentation by proposing Soft-CP, a data augmentation method that improves performance by +26.5% DSC with 10% of data and +10.2% DSC with full data on the KiTS19 dataset.
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.