Mo Data, Mo Problems: How to share big data with ease

Sharing Image Data

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

A Quantitative Path to Pathology

This post was written by a guest author, Kun-Hsing Yu, who can be reached at 

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

Quantifying microscopy images: top 10 tips for image acquisition

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.

Raw Image

Corrected image, using a CellProfiler image analysis pipeline for spot detection

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It is plane to Z how FIJI is a great companion for visually verifying CellProfiler’s 3D segmentation

Now that CellProfiler can handle 3D images, how do you view the results? When processing 2D images, the IdentifyPrimaryObjects module will produce a figure that displays the original image next to the segmented image to make validation fast and convenient, a side-by-side comparison. However, the extra dimension in 3D complicates this approach, because the 3D image must be transformed in some manner in order to appear on a flat surface (e.g. the monitor or phone you’re using to read this blog post!). This blog post contains advice on how to look at 3D images and the 3D segmentation image produced by CellProfiler and introduces orthoviews in FIJI. If you would like to explore the example used in this post please check out the demo posted on the CellProfiler forum. Continue reading

Screening a million compounds for the price of a few thousand?

Biologists are coming up with more and more complex physiologically-relevant assay systems and scaling them up for screens. From co-cultured cells to C. elegans to 3D organoids and tumor spheroids, these assay systems can be challenging, expensive, lower-throughput, and/or rely on materials such as human primary cells that are in short supply.

Might there be a shortcut allowing you to screen a huge chemical library without the expense? Continue reading

Help! Why do my output images seem all black?

Double clicking on the output images produced by CellProfiler sometimes opens up a screen in your operating system’s default image viewer that looks all black. This can make it seem like your pipeline didn’t work or didn’t produce the right output. However, this can happen for a couple of reasons:

(a) If you’re exporting objects and have only a few objects in your image
(b) If you’re exporting 16-bit images

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Be a histology hero with CellProfiler

Thanks to the rapid advancement in image processing, we now have so many techniques to characterize cellular and subcellular objects (hooray CellProfiler!) Measuring cultured cells in monolayers is (usually) easy…but what about examining how cells interact with each other and their surroundings? Such experiments are often conducted using highly confluent cell cultures, tissue sections, or densely cell-packed organoids. At this level, clusters of cells gather, tightly bind and overlap to form cell niches, and often in a single area multiple clusters of various differentiated cell types can be found with different morphologies and functions. Recognizing and profiling individual cells can be very challenging under these circumstances. Continue reading

CellProfiler & Ilastik: Superpowered Segmentation

Joining forces

CellProfiler is capable of accurate and reliable segmentation of cells by utilizing a broad collection of classical image processing methods. Peruse the documentation on the IdentifyPrimaryObjects module, for example, to get a sense of these, e.g., thresholding, declumping, and watershed. However, despite the many problems CellProfiler can readily solve, certain types of images are particularly challenging. For instance, when the biologically relevant objects are defined more by texture and context than raw intensity many classical image processing techiques can be foiled; DIC images of cells are a common biological example.

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Help! Why does CellProfiler say it can’t find any valid image sets?

Defining the input to CellProfiler can be the hardest part of getting your pipeline set up and your analysis underway.  Incoming images are configured in the first 4 modules of CellProfiler – Images, Metadata, NamesAndTypes, and Groups – which offer lots of flexibility. But it’s sometimes confusing what each one does, and it’s not always obvious which ones you need for your experiment. Continue reading

Making it easier to run image analysis in the cloud: announcing Distributed-CellProfiler

There’s nothing more exciting than getting back a big batch of data from your automated microscope – finally, you have the results of your screen, your timelapse, or whatever you’ve spent the last weeks or months preparing.  That excitement can turn to sadness quickly though when you realize that neither your laptop nor the old general-use computer in the lab are up to analyzing thousands (or tens of thousands, or hundreds of thousands!) of images.  But, congratulations! You’ve reached an elite level of CellProfiler users when you outgrow processing on a single local computer. Continue reading