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AI Algorithm Identifies Lung Tumors Faster Than Other Methods

By Medimaging International staff writers
Posted on 19 Mar 2019
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Image: The new algorithm is designed to identify lung tumors from MRI scans, potentially helping reduce damage to healthy tissue during radiation treatment (Photo courtesy of Shutterstock).
Image: The new algorithm is designed to identify lung tumors from MRI scans, potentially helping reduce damage to healthy tissue during radiation treatment (Photo courtesy of Shutterstock).
Computing scientists at the University of Alberta (Alberta, Canada) have developed a neural network that outperforms other state-of-the-art methods of identifying lung tumors from MRI scans—creating the potential to help reduce damage to healthy tissue during radiation treatment.

Targeting lung tumors using MRI scans is quite challenging as they move significantly when the patient breathes and the scans can also be difficult to interpret. The researchers “trained” the neural network on a set of MRI scans in which doctors had earlier identified lung tumors. It then processed an enormous set of images to learn what tumors look like and what properties they share. The neural network was then tested against scans that may or may not contain tumors. After the neural network was trained, the researchers tested it against another recently developed technique by comparing the two systems with manual tumor identification by an expert radiation oncologist. The new algorithm outperformed the other recent technique in every evaluation measure used by the researchers.

“Algorithms like the one developed in our laboratory can be used to generate a patient-specific model for diagnosis and surgical treatment,” said Pierre Boulanger, Cisco Research Chair in Healthcare Solutions at the University of Alberta. “The tumor regions in scan results can be very subtle, and even once identified, need to be tracked over time as the tumor moves with breathing. The new algorithm is able to combine many possibilities to find the best descriptors to identify unhealthy regions in a scan.”

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