MIRIAM: A machine and deep learning single‐cell segmentation and quantification pipeline for multi‐dimensional tissue images
Eliot T. McKinley, Justin Shao, Samuel T. Ellis, Cody N. Heiser, Joseph T. Roland, Mary C. Macedonia, Paige N. Vega, Susie Shin, Robert J. Coffey, Ken S. Lau
Abstract
Increasingly, highly multiplexed tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is the lack of robust cell segmentation tools for tissue sections. We present Multiplexed Image Resegmentation of Internal Aberrant Membranes (MIRIAM) that combines (a) a pipeline for cell segmentation and quantification that incorporates machine learning-based pixel classification to define cellular compartments, (b) a novel method for extending incomplete cell membranes, and (c) a deep learning-based cell shape descriptor. Using human colonic adenomas as an example, we show that MIRIAM is superior to widely utilized segmentation methods and provides a pipeline that is broadly applicable to different imaging platforms and tissue types.