U. MICHIGAN (US) — A newly developed software tool will make detecting abnormalities in cell and tissue samples faster, more accurate, and more consistent.
The technique, known as Spatially-Invariant Vector Quantization (SIVQ), pinpoints cancer cells and other critical features from digital images made from tissue slides and is not limited to a particular area of medicine.
It can separate calcifications from malignancies in breast tissue samples, search for and count particular cell types in a bone marrow slide, or identify the cherry red nucleoli of cells associated with Hodgkin’s disease, according to a new study, published in the Journal of Pathology Informatics.
“The fact that the algorithm operates effortlessly across domains and lengths scales, while requiring minimal user training, sets it apart from conventional approaches to image analysis,” says Ulysses Balis, associate professor of pathology informatics at the University of Michigan.
The technology differs from conventional pattern recognition software by basing its core search on a series of concentric, pattern-matching rings, rather than the more typical rectangular or square blocks, taking advantage of the rings’ continuous symmetry and allowing for the recognition of features no matter how they’re rotated or whether they’re reversed, like in a mirror.
“That’s good because in pathology, images of cells and tissue do not have a particular orientation,” Balis says. “They can face any direction.”
How it works
SIVQ works with a user starting a search by selecting a small area of pixels, known as a vector, to match elsewhere in the image. The vector can also come from a stored library of images.
The algorithm then compares the circular vector to every part of the image. And at every location, the ring rotates through millions of possibilities in an attempt to find a match in every possible degree of rotation. Smaller rings within the main ring can provide an even more refined search. The program then creates a heat map, shading the image based on the quality of match at every point.
This technique wouldn’t work with a square or rectangular-shaped search structure because those shapes don’t remain symmetrical as they rotate, Balis explains.
The technology has the potential to be a “game changer” for the field by opening myriad new possibilities for deeper image analysis, says Jason Hipp, a pathology informatics research fellow and co-lead author of the paper.
“It’s going to allow us to think about things differently. We’re starting to bridge the gap between the qualitative analysis carried out by trained expert pathologists with the quantitative approaches made possible by advances in imaging technology.”
For example, the most common way to look at tissue samples is still a staining technique that dates back to the1800s. Reading these complex slides and rendering a diagnosis is part of the art of pathology.
SIVQ, however, can assist pathologists by pre-screening an image and identifying potentially problematic areas, including subtle features that may not be readily apparent to the eye. Its efficiency in pre-identifying potential problems becomes apparent when one considers that a pathologist may review more than 100 slides in a single day.
“Unlike even the most diligent humans, computers do not suffer from the effects of boredom or fatigue,” Balis says.
Vectors can also be pooled to create shared libraries—a catalog of reference images upon which the computer can search—Balis explains, which could help pathologists to quickly identify rare anomalies.
“Bringing such tools into the clinical workflow could provide a higher level of expertise that is distributed more widely, and lower the rate at which findings get overlooked.”
The software may also help with the analysis of “liquid biopsies,” an experimental technique of scanning blood samples for tiny numbers of cancer cells hiding amid billions of healthy ones.
“No one is talking about replacing pathologists any time soon,” Balis says. “But working in tandem with this technology, the hope is that they will be able to achieve a higher overall level of performance.”
Researchers from Rutgers, Harvard University and Massachusetts General Hospital are contributing to the research.
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