TY - JOUR T1 - Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images JF - bioRxiv DO - 10.1101/790162 SP - 790162 AU - Eliot T McKinley AU - Joseph T Roland AU - Jeffrey L Franklin AU - Mary Catherine Macedonia AU - Paige N Vega AU - Susie Shin AU - Robert J Coffey AU - Ken S Lau Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/10/02/790162.abstract N2 - 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. ER -