CellProfiler 3.0 release: faster, better, and 3D

We are thrilled to announce that CellProfiler 3.0 is now released!  Download it here.

Eighteen months in the making, this is the first version of CellProfiler that can identify objects in 3D images volumetrically – the result of a collaboration with the Allen Institute for Cell Science who funded the project together with NIH. If you’ve not yet seen it, the Allen Cell Explorer is a real visual and biological treat! So many researchers require completely automated analysis of 3D images, as more complex cell organoids are entering mainstream research. The new capabilities of CellProfiler aim to address this growing need.

Although there are no massive changes in the remainder of CellProfiler’s interface, a LOT has improved under the hood since the last release. The thousands of researchers using CellProfiler will probably notice the change in speed though: not just startup speed but also roughly 2-fold improvement in the time it takes to process a typical pipeline. It adds up particularly if you are paying for cloud computing resources! Speaking of which, we recently created Distributed-CellProfiler, which allows running jobs on Amazon Web Services, even if you’re not a computational expert.

We’ve made substantial progress simplifying CellProfiler’s installation. In addition to the macOS and Windows releases of CellProfiler we’ve started packaging a CellProfiler release for Linux that will ease installation across Linux distributions. We’ve also started packaging CellProfiler for a variety of formats, for example, a Python wheel is now available from the Python Package Index and a Docker image is now available from Docker Hub. In an effort to see new uses for CellProfiler we’ve made CellProfiler much simpler to compile on a variety of familiar and unusual platforms by requiring fewer dependencies and only using ubiquitous build systems.

Those taking a peek at the code will notice 3.0 is massively trimmed down, in part due to better integration with Python’s scientific community. We’ve contributed most of CellProfiler’s fundamental image analysis, image processing, and image segmentation algorithms to scikit-image making them readily available as a package to those writing their own imaging applications.

And for the machine learning enthusiasts out there, CellProfiler is the first biologist-friendly software we are aware of that can integrate deep learning! You might have noticed convolutional neural networks in the news, as they’ve been massively successful lately, especially for computer vision tasks. There are now preliminary demonstrations of using a TensorFlow and a Caffe model with CellProfiler. Now, there are some major caveats: some do-it-yourself installations are required, and certain deep learning frameworks are compatible only with certain operating systems. Thus, running these models requires more expertise than the typical CellProfiler pipeline. Our lab created a CellProfiler module, MeasureImageFocus in collaboration with Google Accelerated Sciences, who trained a model to detect focus in images. It’s available as a plugin (download the module and see instructions for its use). Meanwhile, the Wählby lab at Uppsala University created a CellProfiler module that enables running a pretrained module for cell segmentation within a CellProfiler pipeline. The module is called CellProfiler-Caffe bridge (download and install as described in the supplementary note to their paper).

Perhaps most importantly, we’ve done a total review of all of CellProfiler’s help buttons and manual. Its prior version was already the highest-ranking biological imaging software for usability and functionality in an independent review! Designed by and for biologists, CellProfiler equips you with powerful computational tools via a friendly and educational user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.