Enhanced cell segmentation with limited training datasets using cycle generative adversarial networks.

Area of Science:

• Bioimage Analysis
• Deep Learning
• Computational Biology

Background:

• Deep learning significantly advances bioimage analysis but requires large annotated datasets for cell segmentation.
• Limited availability of diverse annotated datasets hinders the development of robust single-cell segmentation models.

Purpose of the Study:

• To develop a novel deep learning architecture, cGAN-Seg, to enhance cell segmentation model training using limited annotated datasets.
• To improve the accuracy and generalization capabilities of cell segmentation models through synthetic data generation.

Main Methods:

• Introduced cGAN-Seg, a CycleGAN-based architecture for generating annotated synthetic microscopy images.
• Synthetic images mimic real-world morphological details, increasing training data variability.
• Evaluated cGAN-Seg's impact on the performance of standard cell segmentation models.

Main Results:

• cGAN-Seg significantly improved the performance of established cell segmentation models compared to conventional training.
• Generated synthetic images closely replicated the nuances of real phase-contrast and fluorescent microscopy images.
• Enhanced training data variability led to improved predictive accuracy and model generalization.

Conclusions:

• cGAN-Seg effectively addresses the challenge of limited annotated data in single-cell segmentation.
• The method accelerates the development of accurate and generalizable microscopy image analysis tools.
• This approach holds potential for advancing foundation models in bioimage analysis through efficient training.