Pembroke College Scholarship
Machine Learning Methods for the Automation of Cell Image Analysis
The vast amount of microscopy data that needs to be analysed in cell-based experiments demand the use of automatic methods, which must be accurate and reliable while providing quantitative information. It has been shown that, in combination with the appropriate imaging platforms and labelling techniques, image analysis methods can automatically provide the quantitative information that is required to better understand complex biological systems. The areas of application are numerous; for example, drug discovery, tissue engineering, genomics and proteomics are some of the sciences that greatly benefit, and to some extent, depend on accurate, fast and intelligent cellular image analysis algorithms. In many cases, cellular image analysis is based on a series of common tasks that have been studied in the computer vision and machine learning community, and that are still actively researched. Those tasks include object detection, segmentation, tracking, counting and classification. My Current research is in the exploration of such methods to develop intelligent systems for the automatic analysis of experiments based on cellular imaging.