Abstract
Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining such datasets remains challenging due to significant variations in patient populations, illumination conditions, and acquisition system characteristics. In this work, we propose S-SYNTH, the first knowledge-based, adaptable open-source skin simulation framework to rapidly generate synthetic skin, 3D models and digitally rendered images, using an anatomically inspired multi-layer, multi-component skin and growing lesion model. The skin model allows for controlled variation in skin appearance, such as skin color, presence of hair, lesion shape, and blood fraction among other parameters. We use this framework to study the effect of possible variations on the development and evaluation of AI models for skin lesion segmentation, and show that results obtained using synthetic data follow similar comparative trends as real dermatologic images, while mitigating biases and limitations from existing datasets including small dataset size, lack of diversity, and under-representation.
A. Kim, N. Saharkhiz and E. Sizikova—These authors contributed equally to this work.
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Notes
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Code and supporting data are available at: https://github.com/DIDSR/ssynth-release.
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Acknowledgments
We thank anonymous reviewers for helpful suggestions. We thank the OpenHPC and RST teams (OSEL/CDRH/FDA) for providing help with experiments and data release. This is a contribution of the US Food and Drug Administration and is not subject to copyright. The mention of commercial products herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.
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Kim, A. et al. (2024). S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_69
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