CellProfiler 4 is a new version of the open-source image analysis software that offers improved speed, usability, and functionality. Developed by the Broad Institute, CellProfiler is used by researchers to process microscopy images into interpretable measurements. The software has been widely adopted and is currently referenced over 2000 times per year. CellProfiler 4 was developed with user feedback in mind, incorporating several user interface refinements to improve usability. New modules have been introduced to expand the software's capabilities, and performance has been optimized to reduce the time and cost associated with running large-scale analysis pipelines. The software has been restructured to improve performance, reliability, and utility, and is now available for download at cellprofiler.org. The software was originally written in MATLAB but was rewritten in Python 2, which was then updated to Python 3. This allowed for a broader restructuring of the software's code. CellProfiler 4 is now available as two separate packages: cellprofiler and cellprofiler-core. The cellprofiler-core package contains all the critical functionality needed to execute CellProfiler pipelines, while the cellprofiler repository now primarily contains the user interface code and built-in modules. Several user interface refinements have been made to make the software more accessible and easier to use. These include a new 3D viewer, expanded figure contrast dialogs, and an interface to visualize which modules produce inputs needed by, or use outputs from, a module of interest. Other changes make it easier to develop and configure pipelines. New and restored features include the CombineObjects module, which allows users to merge sets of objects which have been defined separately. The RunImageJMacro module has been introduced to replace the RunImageJ module, allowing users to access ImageJ functions and plugins within a CellProfiler pipeline. Several existing modules have also been upgraded, including the Threshold module, which now allows all pre-existing threshold strategies to be used in 'adaptive' mode. New measurements have been added to the software, including measurements now available in scikit-image, such as bounding box locations, image moments and inertia tensors. These new features may be of particular value for training machine learning models. Performance improvements have been made to the software, including optimizations to the startup sequence and file loading processes. These improvements have significantly reduced the time needed to add large folders of images to the file list. In addition, the software has been optimized for performance in complex workflows such as 3D segmentation and the Cell Painting assay. The software has also been optimized for performance in the MeasureColocalization module, which is used to measure the Costes Colocalization Coefficient. This module has been optimized to reduce the time taken to process images, and three different strategies have been introduced to improve performance. Overall, 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.CellProfiler 4 is a new version of the open-source image analysis software that offers improved speed, usability, and functionality. Developed by the Broad Institute, CellProfiler is used by researchers to process microscopy images into interpretable measurements. The software has been widely adopted and is currently referenced over 2000 times per year. CellProfiler 4 was developed with user feedback in mind, incorporating several user interface refinements to improve usability. New modules have been introduced to expand the software's capabilities, and performance has been optimized to reduce the time and cost associated with running large-scale analysis pipelines. The software has been restructured to improve performance, reliability, and utility, and is now available for download at cellprofiler.org. The software was originally written in MATLAB but was rewritten in Python 2, which was then updated to Python 3. This allowed for a broader restructuring of the software's code. CellProfiler 4 is now available as two separate packages: cellprofiler and cellprofiler-core. The cellprofiler-core package contains all the critical functionality needed to execute CellProfiler pipelines, while the cellprofiler repository now primarily contains the user interface code and built-in modules. Several user interface refinements have been made to make the software more accessible and easier to use. These include a new 3D viewer, expanded figure contrast dialogs, and an interface to visualize which modules produce inputs needed by, or use outputs from, a module of interest. Other changes make it easier to develop and configure pipelines. New and restored features include the CombineObjects module, which allows users to merge sets of objects which have been defined separately. The RunImageJMacro module has been introduced to replace the RunImageJ module, allowing users to access ImageJ functions and plugins within a CellProfiler pipeline. Several existing modules have also been upgraded, including the Threshold module, which now allows all pre-existing threshold strategies to be used in 'adaptive' mode. New measurements have been added to the software, including measurements now available in scikit-image, such as bounding box locations, image moments and inertia tensors. These new features may be of particular value for training machine learning models. Performance improvements have been made to the software, including optimizations to the startup sequence and file loading processes. These improvements have significantly reduced the time needed to add large folders of images to the file list. In addition, the software has been optimized for performance in complex workflows such as 3D segmentation and the Cell Painting assay. The software has also been optimized for performance in the MeasureColocalization module, which is used to measure the Costes Colocalization Coefficient. This module has been optimized to reduce the time taken to process images, and three different strategies have been introduced to improve performance. Overall, 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.