Cambridge Research & Instrumentation, Inc. (CRi), a leader in biomedical imaging, launched inForm, an advanced image analysis software based on the company’s proprietary Machine Learning System (MLS). inForm solves biologists’ and cancer researchers’ challenging image analysis problems by combining fast and easy-to-use automated image processing with advanced learning features that enable the user to train the software to process images automatically.
inForm detects and quantifies tissue structures and protein markers within individual cells in tissue sections stained with conventional histochemical and immunohistochemical methods. Through this technology, in combination with CRi’s multispectral imaging systems, cancer researchers and biologists can identify and evaluate multiple markers within a single sample. With conventional analytical approaches, overlapping multiple markers blend together into indistinguishable masses of color. inForm allows researchers to have an unparalleled understanding of complex expressions and interactions within a single tissue sample.
Through its user-friendly interface and short training time, researchers are able to develop reliable algorithms in a matter of minutes, compared to hours or days with other technologies. inForm can also be easily installed on a researcher’s laptop, foregoing the need for expensive blade servers, required by competing technologies.
inForm is currently in use by numerous research and academic organizations, including the University of Pennsylvania and the Dana-Farber Cancer Institute.
“The combination of multispectral imaging and pattern-recognition based image analysis create a powerful tool for extracting key molecular information from tissue samples,” said Massimo Loda, pathology researcher at the Dana Farber Cancer Institute. “The information revealed using CRi’s inForm product enables a wide range of clinically-significant research.”
“We are very excited to be able to offer inForm’s advanced technology to the cancer research and biologist communities,” says CRi President & CEO, George Abe. “Automated image analyses, especially reliable quantitation of proteins in tissue sections, play key roles in pre-clinical and clinical studies. This breakthrough will greatly facilitate this area of research.’
