#Cellprofiler analyzing lines how to
#Cellprofiler analyzing lines install

#Cellprofiler analyzing lines install

Getting startedįor the main part of the analysis, you will need to install Ilastik and CellProfiler.

#Cellprofiler analyzing lines how to

The guide displayed here gives detailed information on how to handle IMC images.įor additional information on CellProfiler, please refer to their manuals. It is recommended to acquire 5 or more channels to avoid potential downstream problems where images are considered to be of the RGBA type (red, green, blue, alpha).txt files (see the pre-processing section) There are some points that need to be considered when using this pipeline: tiff files, Ilastik pixel classification, segmentation and feature extraction can be performed in a technology-agnostic way. While we highlight the use of the pipeline with imaging mass cytometry (IMC) data, the concepts presented here can be easily applied to other highly multiplexed imaging data. 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