Background: Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach-SYNTA-for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.
Methods: The SYNTA method employs a fully parametric approach to create photo-realistic synthetic training datasets tailored to specific biomedical tasks. Its applicability is tested in the context of muscle histopathology and skeletal muscle analysis. This new approach is evaluated for two real-world datasets to validate its applicability to solve complex image analysis tasks on real data.
Results: Here we show that SYNTA enables expert-level segmentation of unseen real-world biomedical data using only synthetic training data. By addressing the lack of representative and high-quality real-world training data, SYNTA achieves robust performance in muscle histopathology image analysis, offering a scalable, controllable and interpretable alternative to generative models such as Generative Adversarial Networks (GANs) or Diffusion Models.
Conclusions: SYNTA demonstrates great potential to accelerate and improve biomedical image analysis. Its ability to generate high-quality photo-realistic synthetic data reduces reliance on extensive collection of data and manual annotations, paving the way for advancements in histopathology and medical research.
Image analysis is essential for diagnosing diseases and advancing medical research. However, analyzing these images is challenging due to their complexity and the amount of data they contain. Artificial intelligence (AI) can help to improve this process but developing AI solutions requires large, high-quality training data which can be time-consuming and expensive to obtain. We developed an alternative computational approach called SYNTA to create realistic computer-generated images that can be used as training data for AI systems. We trained an AI system on computer-generated data only and show it can analyze previously unseen real biomedical images as accurately as human experts. This approach could make biomedical research easier, hence speeding up the process of developing new methods to detect disease from images.
© 2025. The Author(s).