We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

MedImaging

Download Mobile App
Recent News Radiography MRI Ultrasound Nuclear Medicine General/Advanced Imaging Imaging IT Industry News

AI Algorithm Identifies Lung Tumors Faster Than Other Methods

By MedImaging International staff writers
Posted on 19 Mar 2019
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.”

Related Links:
University of Alberta


Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Ultrasound System
Acclarix AX9
New
C-Arm with FPD
Digiscan V20 / V30
Compact C-Arm with FPD
Arcovis DRF-C R21

Latest Industry News News

Samsung and Bracco Enter Into New Diagnostic Ultrasound Technology Agreement

IBA Acquires Radcal to Expand Medical Imaging Quality Assurance Offering

International Societies Suggest Key Considerations for AI Radiology Tools