The cell is the basic structural and functional unit of life, with varying sizes, shapes, and densities. There are many different physiological and pathological factors that influence these parameters. It is therefore extremely important for biomedical and pharmaceutical research to study the characteristics of cells. Traditionally, researchers observed cell samples directly through microscopes in order to study the morphological changes of cells. In recent years, with the development of computer science and artificial intelligence, deep learning can now be combined with cell analysis methods. This can replace researchers' direct observation under the microscope and manual interpretation of images, improving the efficiency and accuracy of research.
In recent years, an increasing number of deep learning-based algorithms have been developed to empower cell image analysis, primarily for addressing three key tasks:
For the above three crucial tasks, a review article published in the journal Intelligent Computing discusses in depth the progress of deep learning techniques in the above areas. “In contrast to traditional computer vision techniques, a deep neural network (DNN) can automatically produce more effective representations than handcrafted representations by learning from a large-scale dataset. In cell images, deep learning-based methods also show promising results in cell segmentation and tracking.” Authors said: “Such successful applications demonstrate the ability of DNNs to extract high-level features and shed light on the potential capability of using deep learning to reveal more sophisticated life laws behind cellular phenotypes.”
In addition, the authors also discuss the challenges and opportunities of deep learning methods in cell image processing. Authors said: ”Deep learning has demonstrated an incredible ability to perform cell image analysis. However, there remains a significant performance gap between deep-learning algorithms in academic research and practical applications.” There are currently challenges and opportunities in three aspects, namely data quantity, data quality, and data confidence:
Using deep learning, scientists are exploring new technologies to improve cell image analysis. More effective solutions will be proposed in the future, and deep learning and biomedical research will be more closely integrated.
Intelligent Computing
Deep Learning in Cell Image Analysis
7-Sep-2022