Cookies

Like most websites The Pathologist uses cookies. In order to deliver a personalized, responsive service and to improve the site, we remember and store information about how you use it. Learn more.
Subscribe to Newsletter
Diagnostics Digital and computational pathology, Microscopy and imaging, Oncology

When Pathology Goes 3D

This image shows a 3D pathology dataset of a prostate biopsy stained with a fluorescent analogue of H&E (left). The researchers perform deep learning-based image translation to convert the H&E dataset into a synthetic dataset that looks like it has been immunolabeled to highlight a cytokeratin biomarker (brown) that is expressed by the epithelial cells in all prostate glands. In turn, this synthetically immunolabeled dataset allows for relatively straightforward and accurate 3D segmentation of the prostate gland epithelium (yellow) and lumen spaces (red). This also allows the researchers to extract the “skeleton” of the branching-tree gland network (magenta). Quantitative features derived from these segmented 3D structures are used to train a machine classifier to stratify between aggressive (recurrent) versus indolent (non-recurrent) cancer.

Jonathan Liu, a professor at the University of Washington and senior investigator on this project (1), remarks that “this is the first of hopefully many studies to come that will demonstrate the value of 3D pathology versus conventional 2D pathology for guiding critical treatment decisions for cancer patients. We are combining the best of modern optical technologies, tissue-clearing methods, and big data machine learning tools to transform the field of pathology that is so foundational to diagnostic medicine.”

Click here to see a video summary of their work.

Receive content, products, events as well as relevant industry updates from The Pathologist and its sponsors.
Stay up to date with our other newsletters and sponsors information, tailored specifically to the fields you are interested in

When you click “Subscribe” we will email you a link, which you must click to verify the email address above and activate your subscription. If you do not receive this email, please contact us at [email protected].
If you wish to unsubscribe, you can update your preferences at any point.

  1. W Xie et al., Cancer Res, 82, 334 (2022). PMID: 34853071.
Most Popular
Register to The Pathologist

Register to access our FREE online portfolio, request the magazine in print and manage your preferences.

You will benefit from:
  • Unlimited access to ALL articles
  • News, interviews & opinions from leading industry experts
  • Receive print (and PDF) copies of The Pathologist magazine

Register