Affiliation:
1. University of Chinese Academy of Sciences, China
2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
3. The Chinese University of Hong Kong, China
4. Zhejiang Lab, China
5. Zhejiang University, China
Abstract
Cell images, which have been widely used in biomedical research and drug discovery, contain a great deal of valuable information that encodes how cells respond to external stimuli and intentional perturbations. Meanwhile, to discover rarer phenotypes, cell imaging is frequently performed in a high-content manner. Consequently, the manual interpretation of cell images becomes extremely inefficient. Fortunately, with the advancement of deep-learning technologies, an increasing number of deep learning-based algorithms have been developed to automate and streamline this process. In this study, we present an in-depth survey of the three most critical tasks in cell image analysis: segmentation, tracking, and classification. Despite the impressive score, the challenge still remains: most of the algorithms only verify the performance in their customized settings, causing a performance gap between academic research and practical application. Thus, we also review more advanced machine learning technologies, aiming to make deep learning-based methods more useful and eventually promote the application of deep-learning algorithms.
Funder
Innovation and Technology Fund
Natural Science Foundation of Guangdong Province
National Natural Science Foundation of China
National Basic Research Program of China
Publisher
American Association for the Advancement of Science (AAAS)
Cited by
9 articles.
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