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

• URL: http://arxiv.org/abs/2203.10507v1
• Date: Sun, 20 Mar 2022 09:40:04 GMT
• Title: Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions
• Authors: Pingping Dai, Licong Dong, Ruihan Zhang, Haiming Zhu, Jie Wu, Kehong Yuan
• Abstract summary: We propose a new object-blend method that combines the Copy-Paste augmentation method for semantic segmentation of medical lesions offline. In our experiments on the KiTS19[2] dataset, Soft-CP outperforms existing medical lesions synthesis approaches.
• Score: 4.001984499227037
• License: http://creativecommons.org/licenses/by/4.0/
• 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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