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. 2023 May 5;13:7303. doi: 10.1038/s41598-023-34341-2

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison of the performance of the Swin UNETR in terms of the Dice score for breast segmentation when supplied with synthetic image data (n = 2000) during a self-supervised pre-training setting (“With pre-training”) and when no pre-training (“No pre-training”) is performed. After the optional self-supervised pre-training step, the model is finetuned with 5%, 10%, 20%, 40%, 80%, or 100% of the available internal data with corresponding breast segmentation outlines used as the ground truth (n = 200). We find that the synthetic data generated using a dataset of another institution can substantially improve the Dice score on the internal dataset when only limited data are available for training. Error bars show the standard deviation extracted through bootstrapping (n = 1000).