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. 2021 Sep 10;22(1):433.
doi: 10.1186/s12859-021-04344-9.

CellProfiler 4: improvements in speed, utility and usability

David R Stirling  1 , Madison J Swain-Bowden  2 , Alice M Lucas  1 , Anne E Carpenter  1 , Beth A Cimini #  3 , Allen Goodman #  1
Affiliations

CellProfiler 4: improvements in speed, utility and usability

David R Stirling et al. BMC Bioinformatics. .

Abstract

Background: Imaging data contains a substantial amount of information which can be difficult to evaluate by eye. With the expansion of high throughput microscopy methodologies producing increasingly large datasets, automated and objective analysis of the resulting images is essential to effectively extract biological information from this data. CellProfiler is a free, open source image analysis program which enables researchers to generate modular pipelines with which to process microscopy images into interpretable measurements.

Results: Herein we describe CellProfiler 4, a new version of this software with expanded functionality. Based on user feedback, we have made several user interface refinements to improve the usability of the software. We introduced new modules to expand the capabilities of the software. We also evaluated performance and made targeted optimizations to reduce the time and cost associated with running common large-scale analysis pipelines.

Conclusions: CellProfiler 4 provides significantly improved performance in complex workflows compared to previous versions. This release will ensure that researchers will have continued access to CellProfiler's powerful computational tools in the coming years.

Keywords: Bioimaging; Image analysis; Image quantitation; Image segmentation; Microscopy.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
User interface refinements in CellProfiler 4. a The new 3D viewer window with plane controls in the top right. b Contrast and normalization adjustment popup available with any image window. c Interface displayed when the “trace” command is called on a module. Arrow icons on the left represent modules which provide data to or use data from the selected module (dot icon). d Selection widget for choosing multiple images for analysis. Images sourced from disabled or missing modules are highlighted. e “Add modules” pane in search mode, the modules list is filtered based on the text entered into the search box. f The Workspace Viewer module displaying a custom overlay of data from the example pipeline
Fig. 2
Fig. 2
Approaches for combining object sets within the CombineObjects module. Results represent the output from different methods available within the module. “Merge” will join touching objects and distribute conflicting regions to the nearest object from the initial set. “Preserve” will add only regions of objects from the second set which did not overlap with the initial set. “Discard” will only add objects with no overlap. “Segment” will add both object sets and re-segment disputed regions
Fig. 3
Fig. 3
General performance in CellProfiler 3 versus CellProfiler 4. Results represent independent runs on a machine running Windows 10, using 1 worker process. a Time from launching the CellProfiler executable to display of the full GUI (n = 5). b Time taken to run the ExampleFly pipeline in Analysis Mode (n = 3). c Time to run the ExampleFly pipeline in Test Mode (n = 5). d Time to run the 3D monolayer tutorial pipeline in Analysis Mode (n = 3)
Fig. 4
Fig. 4
Module-specific performance improvements. Results from individual module testing on a machine running Windows 10. a Execution time for the MedianFilter module running within the 3D Monolayer pipeline (n = 5). b Execution time when running per-object texture measurements on data from the ExampleFly pipeline (n = 5). c Execution time when running MeasureColocalization on 8-bit images from the ExampleFly pipeline (n = 5)
Fig. 5
Fig. 5
Performance of alternative Costes automated thresholding strategies. Execution times for the MeasureColocalization module performing 1 pairwise comparison with Costes features enabled, using each algorithm on a 8-bit images from the ExampleFly pipeline (n = 6) or b 16-bit images from the example Cell Painting dataset (n = 8). On 16-bit images results from CellProfiler 3 are calculated incorrectly, but shown to illustrate relative performance
Fig. 6
Fig. 6
Performance of selected modules within the Cell Painting assay protocol. Numbers in brackets within panel titles correspond to modules in Additional file 1: Figure S1. a Total module execution time (measured in CPU time) per image set for all modules in the pipeline. b Execution time for the MeasureTexture module per image set. c Execution time for the MeasureImageQuality module per image set. d Execution time for the IdentifyPrimaryObjects module per image set. e Execution time for the MeasureGranularity module per image set. f Execution time for the MeasureObjectSizeShape module per image set
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