Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method. In this study, we demonstrate that diffusion models can effectively generate fully-annotated microscopy image data sets through an unsupervised and intuitive approach, using rough sketches of desired structures as the starting point. The proposed pipeline helps to reduce the reliance on manual annotations when training deep learning-based segmentation approaches and enables the segmentation of diverse datasets without the need for human annotations. This approach holds great promise in streamlining the data generation process and enabling a more efficient and scalable training of segmentation models, as we show in the example of different practical experiments involving various organisms and cell types.

翻译：近期计算机视觉领域的进展显著推动了逼真图像数据的生成，其中去噪扩散概率模型被证明是一种尤为有效的方法。本研究表明，扩散模型能够通过一种无监督且直观的方式，以目标结构的粗略草图作为起点，有效生成全标注的显微图像数据集。所提出的流程有助于减少在训练基于深度学习的分割方法时对人工标注的依赖，并能够在无需人工标注的情况下实现多样化数据集的分割。该技术在简化数据生成流程、实现更高效且可扩展的分割模型训练方面展现出巨大潜力——我们通过涉及不同生物体与细胞类型的多项实际实验验证了其有效性。