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Columbia University licenses 3D segmentation software to Varian

Tue, 05/21/2013 - 8:19am

Columbia University has signed a licensing agreement with Varian Medical Systems for novel imaging software that facilitates 3D segmentation, the process by which anatomical structures in medical images are distinguished from one another—an important step in the precise planning of cancer surgery and radiation treatments.

“Organ- and tumor-specific segmentation is fundamental for proper radiation treatment planning and follow-up in cancer patients,” said Lawrence Schwartz, MD, professor and chair of the Department of Radiology, Columbia University Medical Center, who has extensive experience in both conventional and novel imaging techniques. “Our algorithms have been developed in response to the growing demand for quantitative imaging techniques that provide more accurate organ/tumor delineation and tumor response criteria. At the Computational Image Analysis Laboratory, led by Binsheng Zhao, DSc, professor of clinical radiology, we have incorporated advanced methodologies to address these needs. Columbia is pleased to have established a relationship with Varian, a manufacturer of treatment devices.”

Three-D segmentation of CT and MR images provides a reliable way to identify organs such as the liver, spleen, and kidneys. Determining organ and tumor contours and volumes (including those of primary and metastatic tumors) before, during, and after treatment can be challenging. Accurate and efficient characterization of these diverse structures is necessary to enable noninvasive assessments in clinical practice and clinical trials, as well as in radiation treatment planning.

“Modern radiation treatment planning requires careful delineation of the targeted tissue, as well as the critical structures to be avoided,” said Jeff Amacker, senior director of clinical solutions at Varian. “We hope that our collaborative efforts with Columbia University Medical Center’s radiology department will lead to improved patient care by providing new tools and automation for the precise planning of radiation treatments.”

Source: Columbia University Medical Center

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