Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) has been improved to the point that nanoscale imaging of whole cell and even larger biological samples can now be reliably obtained. The science bottleneck now moves to processing these images to extract insights and knowledge. Librarians at UCR and Virginia Tech are working with microscopy and biomed researchers at Yale School of Medicine to segment cell organelles from recently acquired enhanced FIB-SEM images of mouse neurons. While we did not develop new machine-learning algorithms, practical AI work still requires smart strategies to apply these algorithms to their fullest effects. We will discuss these practical considerations, from ground truth labeling, training, and post-processing, and highlight the necessity to adapt machine-learning pipeline to the needs of the scientific drives.

This project showcases how librarians can be embedded and contribute to faculty research. UCR Library's Research Data Initiative is open to collaborate with UCR faculty on data intensive and AI assisted methods to directly help solve domain research problems. Come and learn about the UCR library's vision on this initiative, and solicit potential collaborative project ideas.

Dr. Zhiwu Xie