CellProfiler 3.0 is a significant update to the open-source image analysis software, designed to support both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, which are increasingly common in biomedical research. The new version improves the infrastructure, introduces cloud-based large-scale image processing capabilities, and enables the use of pretrained deep learning models for image analysis. CellProfiler is designed to be user-friendly for biologists, offering a well-documented interface and powerful computational tools. Key features include: 1. **3D Image Analysis**: Enhanced support for 3D image analysis, including volumetric and plane-wise segmentation and feature extraction. 2. **Deep Learning Integration**: Support for running pretrained deep learning models, such as U-Net for cell segmentation, using frameworks like TensorFlow and Caffe. 3. **Cloud Computing**: A script-based interface, Distributed-CellProfiler, for parallel processing of thousands of images on Amazon Web Services (AWS). 4. **User-Friendly Interface**: Improved documentation and community resources, including tutorials and example workflows. 5. **Performance Improvements**: Faster processing speed compared to previous versions, with improved runtimes for common pipelines. The software is widely used in various biological research areas, contributing to discoveries in drug development, clinical trials, and basic research. Future developments aim to enhance data mining and downstream analytics, integrate with popular notebook tools, and further advance deep learning capabilities in bioimaging.CellProfiler 3.0 is a significant update to the open-source image analysis software, designed to support both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, which are increasingly common in biomedical research. The new version improves the infrastructure, introduces cloud-based large-scale image processing capabilities, and enables the use of pretrained deep learning models for image analysis. CellProfiler is designed to be user-friendly for biologists, offering a well-documented interface and powerful computational tools. Key features include: 1. **3D Image Analysis**: Enhanced support for 3D image analysis, including volumetric and plane-wise segmentation and feature extraction. 2. **Deep Learning Integration**: Support for running pretrained deep learning models, such as U-Net for cell segmentation, using frameworks like TensorFlow and Caffe. 3. **Cloud Computing**: A script-based interface, Distributed-CellProfiler, for parallel processing of thousands of images on Amazon Web Services (AWS). 4. **User-Friendly Interface**: Improved documentation and community resources, including tutorials and example workflows. 5. **Performance Improvements**: Faster processing speed compared to previous versions, with improved runtimes for common pipelines. The software is widely used in various biological research areas, contributing to discoveries in drug development, clinical trials, and basic research. Future developments aim to enhance data mining and downstream analytics, integrate with popular notebook tools, and further advance deep learning capabilities in bioimaging.