A survey on applications of deep learning in microscopy image analysis

• Published in: Computers in biology and medicine 2021-07, Vol.134, p.104523-104523, Article 104523
• Main Authors: Liu, Zhichao, Jin, Luhong, Chen, Jincheng, Fang, Qiuyu, Ablameyko, Sergey, Yin, Zhaozheng, Xu, Yingke
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biomedical research; Classification; Computer science; Convolution; Deep learning; Dynamic characteristics; Image acquisition; Image analysis; Image classification; Image processing; Image segmentation; Iterative algorithms; Iterative methods; Learning algorithms; Localization; Machine learning; Medical diagnosis; Medical research; Microscopy; Neural network; Neural networks; Semantics; Super-resolution microscopy
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104523

Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in signal-to-noise ratios and include a wealth of information which require multiple parameters and time-consuming iterative algorithms for processing. Precise analysis and statistical quantification are often needed for the understanding of the biological mechanisms underlying these dynamic image sequences, which has become a big challenge in the field. As deep learning technologies develop quickly, they have been applied in bioimage processing more and more frequently. Novel deep learning models based on convolution neural networks have been developed and illustrated to achieve inspiring outcomes. This review article introduces the applications of deep learning algorithms in microscopy image analysis, which include image classification, region segmentation, object tracking and super-resolution reconstruction. We also discuss the drawbacks of existing deep learning-based methods, especially on the challenges of training datasets acquisition and evaluation, and propose the potential solutions. Furthermore, the latest development of augmented intelligent microscopy that based on deep learning technology may lead to revolution in biomedical research. •Deep learning-based microscopy image analysis is of great significance for biomedical researches.•The applications of deep learning in image classification, segmentation, tracking and reconstruction are summarized.•The challenges in deep learning (e.g. lack of annotated public datasets) are discussed.•Augmented intelligent microscopy that based on deep learning technology may lead to revolution in biomedical research.