Dr. Ulysses Balis clicks a mouse to identify a helicopter in a satellite photo of Baghdad, Iraq. With another click, an algorithm that he and his team designed picks out three more choppers without highlighting any of the buildings, streets, trees or cars.
Balis isn’t playing war games. The director of the Division of Pathology Informatics at the Medical School is demonstrating the extreme flexibility of a software-tool aimed at making the detection of abnormalities in cell and tissue samples faster, more accurate and more consistent.
In a medical setting, instead of helicopters, the technique, known as Spatially-Invariant Vector Quantization (SIVQ), can pinpoint cancer cells and other critical features from digital images made from tissue slides.
But SIVQ isn’t limited to any particular area of medicine. It readily 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 just 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,” Balis says.
The technology — developed 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.
“That’s good because in pathology, images of cells and tissue do not have a particular orientation,” Balis says. “They can face any direction.”
Additional U-M authors include Dr. Jason Hipp and Dr. Jerome Cheng.
