Utilizing modern machine learning approaches for image cytometry

Abstract

Until recently, the scientific community has lacked image segmentation tools that are precise, reliable, and general-purpose. Such tools are especially needed in applications to bacterial image cytometry, wherein single-pixel precision is needed, perfect segmentation must be achieved over thousands of cells over hundreds of time points, and the approach must be applicable to a diversity of cellular morphologies present in a single micrograph. In this document, I detail the challenges of bacterial image segmentation, the failures of prior approaches, and the use of machine learning to solve this problem in virtually any unilaminar cell imaging context.