MIRIAM: A machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images.

Researchers

Journal

Modalities

Models

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 1) a pipeline for cell segmentation and quantification that incorporates machine learning-based pixel classification to define cellular compartments, 2) a novel method for extending incomplete cell membranes,and 3) 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. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.