Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images
bioRxiv (Cold Spring Harbor Laboratory)2019
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Eliot T. McKinley, Joseph T. Roland, Jeffrey L. Franklin, Mary C. Macedonia, Paige N. Vega, Susie Shin, Robert J. Coffey, Ken S. Lau
Abstract
Abstract Increasingly, highly multiplexed in situ tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is a lack of robust cell segmentation tools applicable for sections of tissues with a complex architecture and multiple cell types. Using human colorectal adenomas, we present a pipeline for cell segmentation and quantification that utilizes machine learning-based pixel classification to define cellular compartments, a novel method for extending incomplete cell membranes, quantification of antibody staining, and a deep learning-based cell shape descriptor. We envision that this method can be broadly applied to different imaging platforms and tissue types.
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