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)、机器学习和深层次学习(DL)方法在生物医学图像分析领域越来越重要。然而,为了充分发挥这些方法的潜力,需要大量具有代表性的实验性获得的图像,其中含有大量人工附加说明的物体,作为培训数据。在这里,我们采用合成数据(SYNTA)(合成数据)作为生成合成、摄影现实和高度复杂的生物医学图像的新办法,作为DL系统的培训数据。我们展示了我们在肌肉纤维和骨科连接组织分析方面的做法的多功能性。我们证明,有可能在以前不见的真实世界数据上执行稳健和专家级的分解任务,而无需仅使用合成培训数据进行人工说明。我们的方法是一种完全的类比技术,是一种可解释和控制的替代基因反转网络(GANs)的方法,并有可能大大加快在微观外的各种生物医学应用中的定量图像分析。