About this Research Topic
Automated cell detection is a very important step in image analysis of cells. Given rapid advancements in microscopy technology, life-science researchers acquire big data in cell biology for breakthrough scientific discovery. However, manual analysis of high-content and high-throughput screening for quantifying and capturing cellular features at a large scale is a formidable task. Such challenge in image analysis of biological cells calls for the aid of artificial intelligence to produce fast and accurate analysis results.
This Research Topic focuses on the use of state-of-the-art deep learning methods for cell detection.
Authors are welcome to submit papers that address the application of deep learning to the following areas, including but not limited to:
- Cell segmentation
- Cell counting
- Dynamic cell tracking
- Cell shape analysis
- Quantification of cell distribution
- Quantification of cell morphology
- Detection of organelles in single cells
Keywords: Cell segmentation, deep learning, big data, microscopy technology, artificial intelligence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.