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 underrepresentation.

本研究针对皮肤影像AI技术开发中数据集获取难题，提出了S-SYNTH，这是一种知识驱动、可适应的开源皮肤仿真框架。通过利用解剖启发的多层多组分皮肤模型，该框架能够快速生成合成皮肤和数字图像，研究表明合成数据在皮肤病变分割AI模型的开发与评估中结果与真实图像相似，从而减轻了现有数据集的偏见和局限性。

基于知识的合成皮肤图像生成：S-SYNTH