I’m excited to announce the release of CellProfiler 3.1.
Our focus for CellProfiler 3.1 was polishing features and squashing bugs introduced in CellProfiler 3.0. We also started laying down the foundation for our next release, CellProfiler 4.0, that will transition CellProfiler from Python 2 to Python 3, improve multiprocessing, and overhaul the interface.
There’re a few noteworthy changes that some users might enjoy like UTF-8 pipeline encoding, a simpler application bundle (that won’t require installing Java), and a variety of documentation improvements.
You can download CellProfiler 3.1 from the cellprofiler.org website. If you have feedback or questions, please let us know on the CellProfiler Forum message one of us on Twitter.
Of course this would not have been possible without the hard work of our software engineers and all our contributors- Allen Goodman, Claire McQuin, Matthew Bowden, Vasiliy Chernyshev, Kyle Karhohs, Jane Hung, Chris Allan, Vito Zanotelli, Carla Iriberri, and Christoph Moehl, take a bow!
As the PI of the Carpenter lab (a.k.a. Broad Institute Imaging Platform, including the CellProfiler team), people often ask how I manage so many ongoing collaborations: we discuss 50+ external projects each year so it is a lot to track! I am happy to reveal our secrets.Continue reading →
Annotated image data is valuable for assessing the performance of an image processing pipeline and as training data for machine learning methods such as deep learning. When assessing the performance of a CellProfiler pipeline, for example a pipeline that segments nuclei, the annotated image data are used as the ground truth. The performance of the pipeline can be quantified by comparing the segmentation output to the ground truth and calculating a comparison metric, such as the Jaccard Index or F1 Score. Annotated images are also essential for deep learning applications as training data, for example see the 2018 Data Science Bowl; an in-depth discussion on how the Data Science Bowl images were annotated can be found on the Kaggle forum. Continue reading →
For those of you who’ve been with us for a long time though, the obvious next question after how to use the new test mode is will my old CellProfiler pipelines work in the new version? We feel the same way – the pipelines you’ve accumulated over the years are precious resources! The good and bad news is that the answer is Yes, mostly. In order to facilitate the speedup and continue the process of streamlining the code, a few things had to go; we also removed some things we felt were causing “option fatigue” for the sake of user friendliness going forward.
Everyone here at the CellProfiler team is very excited about our new 3.0 release, and we certainly hope you are too! CellProfiler 3.0 is much faster than any of our previous releases, and the addition of volumetric processing is a huge game changer. Continue reading →
As described in our lab’s recent review article, there are so many great reasons to use microscopy images to create signatures of perturbations: identifying phenotypes associated with disease, identifying chemical mechanisms of action, and discovering gene functions, among others. Have you wanted to give this approach a try, but been overwhelmed by the computational options available?Continue reading →
At the Imaging Platform we frequently need to send and receive images between collaborators or forum users. Working with images can become challenging simply due to their file size. Memory and disk limitations are often irritants when analyzing images, but in some cases, such as multi-dimensional images and whole-slide scans, images’ file sizes can be large enough that even accessing images becomes a major roadblock, especially when the size starts to rival the size of desktop or laptop hard drives. Continue reading →
This post was written by a guest author, Kun-Hsing Yu, who can be reached at Kun-Hsing_Yu@hms.harvard.edu.
Lung cancer causes more than 1.4 million deaths per year. To diagnose lung cancer, pathologists prepare microscopic slides from surgical or biopsy samples, stain them with appropriate chemicals, and observe the visual patterns of cell morphology under the microscope. This manual (and often laborious) approach is the gold standard for lung cancer diagnosis and distinguished lung cancer subtypes. Continue reading →
Not every image you capture on your microscope is suited for quantification, no matter how nice they may look. Even though you might not notice any problems by eye, the tips outlined here for acquiring and storing images can improve the quality of data derived from digital image analysis. These tips are a bit CellProfiler-centric but generally applicable to any quantification you might do.
Corrected image, using a CellProfiler image analysis pipeline for spot detection