Castro Announces $366K for UTSA Medical Imaging Technology Research
San Antonio, TX – Today, Congressman Joaquin Castro (TX-20) announced $366,250.00 of federal funding from the National Science Foundation (NSF) for the University of Texas at San Antonio (UTSA) to conduct collaborative research for Automated Medical Image Segmentation via Decomposition which allows medical professionals to provide customized medical care to patients. The grants are awarded through the Computing and Communications Foundation (CCF), which advances computing and communication theory and algorithms for computer sciences and the design of computers and software.
“Continuing to advance medical research and technology is an essential part of ensuring Americans have quality and reliable health care,” said Rep. Castro. “UTSA’s research will allow doctors in San Antonio and across the country to spend less time on technical tasks, and create more efficient, customized care for their patients.”
The research project focuses on a novel medical image segmentation algorithm that can be applied to various types of medical images and will be able to be executed by any user with basic computer literacy. In the past, this highly technical, individualized care has required experts to manually analyze the images; however, enormous technological advances have led to a large amount of new and improved medical data. This research will allow medical experts to spend less time analyzing a wide variety of medical images and more time directly working with patients.
“Dr. Matthew Gibson’s work is the kind of basic research that can ultimately improve patient care in the future. This funding will help support a very important area of research particularly for the city of San Antonio, as it is a hub for leading biomedical and healthcare innovation in the country,” added Dr. Bernard Arulanandam, Interim Vice President for Research, UTSA.
This research will be instrumental in developing algorithms that can simultaneously identify all the components for vital organs. The result will be a single algorithm that will be applied to many scenarios and can be executed by non-technical users, making it accessible to more medical professionals and lead to more comprehensive and customized care for patients.
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