This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Frechet Inception Distance (FID) score of 78.47, compared to scores above 83.79) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6694 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.

翻译：本研究提出了Polyp-DDPM，一种基于扩散模型、以掩膜为条件生成逼真息肉图像的方法，旨在增强胃肠道息肉的分割性能。该方法针对医学图像中数据有限、标注成本高昂以及隐私问题等挑战。通过将扩散模型条件设置为代表异常区域的分割掩膜（二值掩膜），Polyp-DDPM在图像质量（弗雷歇初始距离FID得分为78.47，而其他方法得分高于83.79）和分割性能（交并比IoU达到0.7156，而基线模型生成的合成图像低于0.6694，真实数据为0.7067）方面均优于现有最优方法。该方法生成了高质量、多样化的合成数据集用于训练，从而使得息肉分割模型性能可与真实图像相媲美，并提供更强的数据增强能力以改进分割模型。Polyp-DDPM的源代码与预训练权重已在https://github.com/mobaidoctor/polyp-ddpm公开提供。