A survey on applications of deep learning in microscopy image analysis

Area of Science:

• Biomedical Imaging
• Computational Biology
• Microscopy

Background:

• Advanced microscopy generates vast time-lapse image data crucial for understanding biological dynamics.
• Processing these images, often with low signal-to-noise ratios, presents significant challenges in analysis and quantification.
• Current methods require complex parameters and iterative algorithms, hindering biological mechanism discovery.

Purpose of the Study:

• To review the applications of deep learning algorithms in microscopy image analysis.
• To highlight the potential of deep learning in overcoming current bioimage processing limitations.
• To discuss challenges and future directions for deep learning in microscopy.

Main Methods:

• Review of recent literature on deep learning applications in bioimage analysis.
• Focus on convolutional neural network-based models.
• Exploration of applications including image classification, segmentation, tracking, and super-resolution.

Main Results:

• Deep learning models demonstrate significant success in various microscopy image analysis tasks.
• Convolutional neural networks offer promising outcomes for complex bioimage processing.
• Identified challenges include training dataset acquisition and method evaluation.

Conclusions:

• Deep learning is transforming microscopy image analysis, offering powerful tools for biological research.
• Addressing challenges in data and evaluation is key to further advancements.
• Augmented intelligent microscopy, powered by deep learning, promises a revolution in biomedical research.