#LOAD ONE IMAGE AT A TIME CELLPROFILER MANUAL#
#LOAD ONE IMAGE AT A TIME CELLPROFILER SOFTWARE#

#LOAD ONE IMAGE AT A TIME CELLPROFILER SOFTWARE#

To create Tracer, we added visualizations to CellProfiler Analyst that are specific to time-lapse tracking data and that have already been proven useful in the scientific community but have not as yet existed in a single freely-available and open-source software package, namely the following: It emphasizes linking the plotted data to the originating image for visual inspection and improved biological interpretation. We used CellProfiler Analyst as the foundation for Tracer, as CellProfiler Analyst was designed for visualizing large, multi-parametric data sets, with the ability to create various plots of cellular features.

#LOAD ONE IMAGE AT A TIME CELLPROFILER MANUAL#

Thus, Tracer is not itself a tracking algorithm, nor a general-purpose image visualization tool, nor a tool for manual track editing, but may be used in conjunction with other software for those purposes. Although most seamlessly used with data from CellProfiler, the Tracer software is intended as a visualization and quality assessment tool compatible with high-content object tracking data derived from any two-dimensional time-lapse image sets, which most commonly involve fluorescence or brightfield microscopy. We present CellProfiler Tracer to enable the visualization of the rich set of cellular features characteristic of high-content time-lapse assays, as well as to provide measures for assessing track quality.ĬellProfiler Tracer is implemented as part of the CellProfiler Analyst software package (available from ).

However, configuring tracking algorithm parameters has been tedious without a tool to readily assess track quality. CellProfiler is one of the few options for conveniently combining the need for robust cellular identification and the ability to process large numbers of time-lapse movies. Moreover, it includes modular cell-tracking capabilities for time-lapse assays, such as the linear assignment problem (LAP) approach, which provides robust tracking by closing temporal gaps and capturing object merges and splits. The CellProfiler biological image analysis package is widely used for collecting an extensive suite of morphological, intensity, and textural features for cells and organisms in high-content screens. We saw a need for a tool that would link cell images themselves directly to their morphological measurements within a tracking assessment tool without this, a valuable opportunity is missed for the researcher to visually assess important changes in cell morphology and cellular context that accompany particular tracking results. Alternately, commercial software may present such functionality within a polished interface (e.g., Imaris by Bitplane, Volocity by Perkin-Elmer, MetaMorph by Molecular Devices), but such packages are not open-source, precluding access to or adjustment of features and underlying algorithms. Interfaces for review or correction of time-lapse data are sometimes provided in customizable open-source software but are usually manual in nature, requiring visual inspection to detect aberrations. Thus, image and image analysis quality requirements for time-lapse microscopy are more stringent and, due to the volume of data, automated quality control is more necessary. However, time-lapse imaging is acutely susceptible to many artifacts that negatively affect the proper identification and tracking of cells the appearance of such anomalies in a single frame can ruin an entire time series. The combination of automated imaging and large-scale, high-content, live-cell experiments is capable of delivering large amounts of data in very little time. Time-lapse assays probe biological questions that can only be investigated by observing the dynamic behavior of organisms, cells, organelles, or molecular assemblies over time.

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