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This site gives detailed explanations of the individual steps of the pipeline ( see below) to generate single-cell measurements from raw imaging data. The steinbock framework offers a dockerized version of the pipeline and extends the segmentation approach by deepcell segmentation. The pipeline is entirely build on open source tools, can be easily adapted to more specific problems and forms a basis for quantitative multiplexed tissue image analysis.įor a more detailed introduction to IMC as technolgy and common data analysis steps, please refer to the IMC workflow website. The segmentation pipeline is accompanied by the imcsegpipe python package building up on readimc as well as customized CellProfiler modules, which facilitate the analysis of highly multiplexed images. This feature reduction step is followed by standard image segmentation using CellProfiler. It is based on supervised pixel classification using Ilastik to distill segmentation relevant information from multiplexed images in a semi-supervised, automated fashion. This repository presents a flexible and scalable image processing pipeline tailored to highly multiplexed images facilitating the segmentation of single cells across hundreds of images. Measuring objects and their features in images is a basic step in many quantitative tissue image analysis workflows. A flexible multiplexed image segmentation pipeline based on pixel classification