SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
This addresses the bottleneck of data scarcity in biomedical image analysis for researchers and clinicians, offering an interpretable alternative to GANs.
The paper tackles the problem of limited annotated biomedical images for deep learning by introducing SYNTA, a method to generate photo-realistic synthetic training data, and demonstrates it enables expert-level segmentation on real muscle histopathology images without manual annotations.
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.