CellProfiler 3.0 is a new version of the open-source image analysis software designed for biological research. It supports both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, which are increasingly common in biomedical research. The software's infrastructure has been significantly improved, and it now supports cloud-based, large-scale image processing. New plugins allow the use of pretrained deep learning models on images. CellProfiler is designed for biologists, offering powerful computational tools through a well-documented user interface, enabling researchers to create quantitative, reproducible image analysis workflows. The software has been widely adopted by the scientific community, with over 6,000 citations. It provides advanced algorithms for image analysis, organized as individual modules that can be arranged in sequential order to form a pipeline. This pipeline is used to identify and measure cells or other biological objects and their morphological features. CellProfiler's modular design and curated library of image processing and analysis modules benefit biologists in several ways, including reproducibility at scale, flexible feature extraction, and ease of learning. In CellProfiler 3.0, new methods for analyzing 3D images using deep learning architectures and cloud computing resources have been introduced, along with other improvements to usability and capabilities. The software now supports 3D image analysis, including volumetric analysis and plane-wise analysis. It can apply image processing, segmentation, and feature extraction algorithms to entire image volumes, in addition to the more typical iterative and separate analysis of two-dimensional slices from a 3D volume. Whole-volume algorithms consider 3D neighborhoods and incorporate information from surrounding planes, yielding more accurate results, but require more available memory, particularly for large files. CellProfiler 3.0 also supports deep learning, with convolutional neural networks (CNNs) now being used to analyze biomedical images. While CellProfiler does not yet incorporate user-friendly functionalities to train neural networks, various models that have been already trained by researchers can be run inside CellProfiler. Running neural network models requires the installation of certain deep learning frameworks, such as TensorFlow or Caffe. Both TensorFlow and Caffe can easily switch between running on GPUs and traditional central processing units (CPUs) by changing the corresponding configuration. CellProfiler 3.0 also supports cloud computing, with a script-based interface that allows running thousands of batches of images through CellProfiler in parallel on Amazon Web Services (AWS). The software has also been improved in terms of speed, with CellProfiler 3.0's processing speed being faster than version 2.2 on the most common types of pipelines. The software is also more configurable for complex analyses, such as associating cytoplasm regions, transcripts, and other entities to nuclei and measuring a wide variety of morphological properties of each. CellProfiler 3.0 is a mature software serving a large community and making an impact through its thousands of users' biological discoveries. It has been involved in the discovery of potential life-savingCellProfiler 3.0 is a new version of the open-source image analysis software designed for biological research. It supports both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, which are increasingly common in biomedical research. The software's infrastructure has been significantly improved, and it now supports cloud-based, large-scale image processing. New plugins allow the use of pretrained deep learning models on images. CellProfiler is designed for biologists, offering powerful computational tools through a well-documented user interface, enabling researchers to create quantitative, reproducible image analysis workflows. The software has been widely adopted by the scientific community, with over 6,000 citations. It provides advanced algorithms for image analysis, organized as individual modules that can be arranged in sequential order to form a pipeline. This pipeline is used to identify and measure cells or other biological objects and their morphological features. CellProfiler's modular design and curated library of image processing and analysis modules benefit biologists in several ways, including reproducibility at scale, flexible feature extraction, and ease of learning. In CellProfiler 3.0, new methods for analyzing 3D images using deep learning architectures and cloud computing resources have been introduced, along with other improvements to usability and capabilities. The software now supports 3D image analysis, including volumetric analysis and plane-wise analysis. It can apply image processing, segmentation, and feature extraction algorithms to entire image volumes, in addition to the more typical iterative and separate analysis of two-dimensional slices from a 3D volume. Whole-volume algorithms consider 3D neighborhoods and incorporate information from surrounding planes, yielding more accurate results, but require more available memory, particularly for large files. CellProfiler 3.0 also supports deep learning, with convolutional neural networks (CNNs) now being used to analyze biomedical images. While CellProfiler does not yet incorporate user-friendly functionalities to train neural networks, various models that have been already trained by researchers can be run inside CellProfiler. Running neural network models requires the installation of certain deep learning frameworks, such as TensorFlow or Caffe. Both TensorFlow and Caffe can easily switch between running on GPUs and traditional central processing units (CPUs) by changing the corresponding configuration. CellProfiler 3.0 also supports cloud computing, with a script-based interface that allows running thousands of batches of images through CellProfiler in parallel on Amazon Web Services (AWS). The software has also been improved in terms of speed, with CellProfiler 3.0's processing speed being faster than version 2.2 on the most common types of pipelines. The software is also more configurable for complex analyses, such as associating cytoplasm regions, transcripts, and other entities to nuclei and measuring a wide variety of morphological properties of each. CellProfiler 3.0 is a mature software serving a large community and making an impact through its thousands of users' biological discoveries. It has been involved in the discovery of potential life-saving