Spatially-Invariant Vector Quantization for Image Analysis

A new software tool aims to make computer-aided tissue analysis faster, more accurate, and more consistent.

Researchers at the University of Michigan Health System and their colleagues have developed a software tool that aims to make the detection of abnormalities in cell and tissue samples faster, more accurate, and more consistent. The technique, known as Spatially-Invariant Vector Quantization (SIVQ), can pinpoint cancer cells and other critical features from digital images made from tissue slides.

SIVQ interface: A screen capture of the SIVQ graphical user interface is depicted above. The preprocessing viewport in the upper-left demonstrates the source predicate image with this window also being utilized for image navigation. The ring vector preview window, depicted slightly to the right of this viewport allows for visual examination of the selected search predicate. Further to the right are a number of SIVQ algorithm parameter settings (e.g. vector size, quantity of sub-rings, heatmap paint size feature, etc.) that allow for optimization of the algorithm’s overall selectivity and sensitivity. Finally, a post-rendering window is depicted below, with it demonstrating resultant heatmaps, where quality of SIVQ-based pattern matching can be assessed.
SIVQ can separate calcifications from malignancies in breast tissue samples, search for and count particular cell types in a bone marrow slide, or quickly identify the cherry red nucleoli of cells associated with Hodgkin’s disease, according to findings published in the Journal of Pathology Informatics.

The technology — developed by a team led by Ulysses Balis, M.D., director of the Division of Pathology Informatics at the U-M Medical School working in conjunction with researchers at Massachusetts General Hospital and Harvard Medical School — 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. This approach takes advantage of the rings’ continuous symmetry, allowing for the recognition of features no matter how they’re rotated or whether they’re reversed, like in a mirror.

In SIVQ, a search starts with the user selecting a small area of pixels, known as a vector, which he or she wants to try to match elsewhere in the image. The vector can also come from a stored library of images.

The algorithm compares this circular vector to every part of the image. 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 would not work with a square or rectangular- shaped search structure because those shapes don’t remain symmetrical as they rotate.

The technology has the potential open a myriad of new possibilities for deeper image analysis. For example, the most common way to look at tissue samples is still a staining technique that dates back to the 1800s. 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.

SIVQ’s efficiency in pre-identifying potential problems becomes apparent when one considers that a pathologist may review more than 100 slides in a single day.

Vectors can also be pooled to create shared libraries, a catalog of reference images upon which the computer can search, which could help pathologists to quickly identify rare anomalies.

Balis and his colleagues have a number of of additional research projects involving SIVQ nearing completion. These demonstrate the technology’s potential usefulness in a number of basic science and clinical applications. These efforts involve collaborations with researchers at the National Institutes of Health, Mayo Clinic, Rutgers University, Harvard Medical School, and Massachusetts General Hospital.

This technology was done by University of Michigan Health System, Ann Arbor, MI. U-M is seeking licensing partners to bring this technology to market. For more information and to watch videos of the technology in action, visit http://www.uofmhealth.org/News/sivq_pathology_0200.

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