Sequences of microscopy images of live cells are analyzed by cell biologists to understand cellular processes, for example, to prevent cancer or design bio-materials for wound healing. Research progress is slowed or compromised when scientists find the image analysis efforts too labor-intensive to do themselves and the automation methods too numerous, unreliable, or difficult to use. The project develops image-analysis software to leverage human and computer resources together, in particular on the internet, to create high-quality image interpretations. Live-cell imaging studies support basic research to understand cellular processes and design biomaterials. The work on statistically significant performance evaluation can have broad impact on the research methodology in computer vision.
The research explores how human and computer resources can be leveraged together, in particular on the internet, to interpret images and videos of cells. Initially, an expansive benchmark study of detection, segmentation, and tracking algorithms for analyzing images of live cells is conducted. Computer-vision approaches to address the major challenges for existing algorithms are then developed, for example, to interpret the emergence of new cells due to mitosis in time-lapse microscopy videos. Methods are designed for quantifying the quality of automatically and manually obtained annotations and the variability between multiple annotations. A tool is built to effectively and efficiently use the expertise of domain specialists, in particular, cell biologists, to judge and select automated methods that analyze cell images. Crowd-sourcing experiments in which internet workers analyze images are designed and conducted. The quality of these lay workers' annotations is compared to the quality of annotations by domain experts and automated methods. Finally, a machine learning system is developed that automatically determines which types of cell images or videos can be analyzed accurately with or without human involvement.
