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arXiv · 2203.10507

Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions

Pingping Dai·Licong Dong·Ruihan Zhang·Haiming Zhu·Jie Wu·Kehong Yuan
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Abstract

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.

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Pingping Dai, Licong Dong, Ruihan Zhang, Haiming Zhu, Jie Wu, Kehong Yuan. 2022-03-20. Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions. https://arxiv.org/abs/2203.10507

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