Artificial intelligence (AI), machine learning, and deep learning (DL) methods are becoming increasingly important in the field of biomedical image analysis. However, to exploit the full potential of such methods, a representative number of experimentally acquired images containing a significant number of manually annotated objects is needed as training data. Here we introduce SYNTA (synthetic data) as a novel approach for the generation of synthetic, photo-realistic, and highly complex biomedical images as training data for DL systems. We show the versatility of our approach in the context of muscle fiber and connective tissue analysis in histological sections. We demonstrate that it is possible to perform robust and expert-level segmentation tasks on previously unseen real-world data, without the need for manual annotations using synthetic training data alone. Being a fully parametric technique, our approach poses an interpretable and controllable alternative to Generative Adversarial Networks (GANs) and has the potential to significantly accelerate quantitative image analysis in a variety of biomedical applications in microscopy and beyond.

翻译：人工智能（AI）、机器学习与深度学习方法在生物医学图像分析领域日益重要。然而，要充分发挥此类方法的潜力，需要大量包含手动标注对象的代表性实验采集图像作为训练数据。本文提出SYNTA（合成数据）这一创新方法，用于生成合成、照片级逼真且高度复杂的生物医学图像，作为深度学习系统的训练数据。我们通过组织切片中的肌纤维与结缔组织分析案例展示了该方法的普适性。研究证明，仅使用合成训练数据即可在未见过的真实数据上实现稳健且达到专家水平的分割任务，无需手动标注。作为全参数化技术，本方法为生成对抗网络提供了可解释、可控的替代方案，有望显著加速显微成像及其他生物医学应用中的定量图像分析。