Close

Search

Search Menu
Photonics Media Photonics Buyers' Guide Photonics Spectra BioPhotonics EuroPhotonics Vision Spectra Photonics Showcase Photonics ProdSpec Photonics Handbook
More News

Hybrid Microscope Improves Tissue Pathology

Facebook Twitter LinkedIn Email Comments
CHAMPAIGN, Ill., Feb. 13, 2020 — Researchers at the University of Illinois at Urbana-Champaign have paired infrared capabilities with high-resolution optical microscopy and machine learning to bring cancer diagnostics into the digital era.
This side-by-side comparison of a breast tissue biopsy demonstrates some of the infrared-optical hybrid microscope’s capabilities. On the left, a tissue sample dyed by traditional methods. Center, a computed stain created from infrared-optical hybrid imaging. Right, tissue types identified with infrared data. The pink in this image signifies malignant cancer. Courtesy of Rohit Bhargava, University of Illinois.
This side-by-side comparison of a breast tissue biopsy demonstrates some of the infrared-optical hybrid microscope's capabilities. On the left, a tissue sample dyed by traditional methods. Center, a computed stain created from infrared-optical hybrid imaging. Right, tissue types identified with infrared data. The pink in this image signifies malignant cancer. Courtesy of Rohit Bhargava, University of Illinois.

The researchers developed the microscope by adding an infrared laser and a specialized microscope lens called an interference objective to an optical camera. The infrared-optical hybrid measures both infrared data and a high-resolution optical image with a light microscope, the most common type of microscope in clinics and labs.

“We built the hybrid microscope from off-the-shelf components. This is important because it allows others to easily build their own microscope or upgrade an existing microscope,” said Martin Schnell, a postdoctoral fellow in Bhargava research group and first author of the paper describing the research.

With the hybrid microscope, researchers were able to create digital biopsies that closely correlated with traditional pathology techniques and outperformed state-of-the-art infrared microscopes.

Combining the techniques allows users to harness the high-resolution, large field of view and accessibility of an optical microscope, and the infrared data allows computational analysis without the need for dyes or stains that can damage tissues. Software is able to re-create different stains or even overlap them to create a more complete, all-digital picture of the tissue’s composition.

Traditional tissue pathology involves adding dyes or stains so that pathologists can discern the shapes and patterns of cells under a microscope. However, it can be difficult to distinguish cancer from healthy tissue or to pinpoint the boundaries of a tumor, and in many cases diagnosis can be subjective.

“For more than a century, we have relied on adding dyes to human tissue biopsies to diagnose tumors. However the shape and color induced by the dye provide very limited information about the underlying molecular changes that drive cancer,” team leader Rohit Bhargava said, professor of bioengineering and the director of the Cancer Center at Illinois.

The infrared-optical hybrid was able to achieve 10× larger coverage, greater consistency, and 4× higher resolution while allowing infrared imaging of larger samples and in less time and more detail as compared with state-of-the-art infrared microscopes.

“Infrared-optical hybrid microscopy is widely compatible with conventional microscopy in biomedical applications,” Schnell said. “We combine the ease of use and universal availability of optical microscopy with the wide palette of infrared molecular contrast and machine learning. And by doing so, we hope to change how we routinely handle, image, and understand microscopic tissue structure.”

The researchers plan to continue refining the computational tools used to analyze the hybrid images. They are working to optimize machine-learning programs that can measure multiple infrared wavelengths, creating images that readily distinguish between multiple cell types, and integrate that data with the detailed optical images to precisely map cancer within a sample. They also plan to explore further applications for hybrid microscope imaging, such as forensics, polymer science, and other biomedical applications.

Photonics.com
Feb 2020
Research & TechnologyMicroscopyopticsinfraredcancer diagnosticstissue pathologymachine learningBiophotonics

Comments
back to top
Facebook Twitter Instagram LinkedIn YouTube RSS
©2020 Photonics Media, 100 West St., Pittsfield, MA, 01201 USA, [email protected]

Photonics Media, Laurin Publishing
x We deliver – right to your inbox. Subscribe FREE to our newsletters.
We use cookies to improve user experience and analyze our website traffic as stated in our Privacy Policy. By using this website, you agree to the use of cookies unless you have disabled them.