Canadian Healthcare Technology Logo
  • Issues
    • Current Print Issue
    • Print Archive
  • Advertise
    • Publishing Schedule
    • Circulation
    • Unit Sizes and Rates
    • Mechanical Requirements
    • Electronic Advertising
    • White Papers
  • Subscribe
    • Print Edition
    • e-Messenger
    • White Papers
  • Events
  • Vendors
  • About Us

GE Revolution Ascend

GE Revolution Ascend

Enovacom EPC

Enovacom EPC

Imaging News

Neural network for spotting lung tumours devised

March 20, 2019


Pierre BoulangerEDMONTON – Computer scientists at the University of Alberta have developed a neural network that outperforms other state-of-the-art methods of identifying lung tumours from MRI scans.

“Algorithms like the one developed in our laboratory can be used to generate a patient-specific model for diagnosis and surgical treatment,” explained Pierre Boulanger (pictured), Cisco Research Chair in Healthcare Solutions at the U of A.

“A patient-specific model helps with surgical planning. But in order to create such a model, one needs to take medical imaging data and turn it into something one can simulate,” he added.

Targeting lung tumours using MRI scans is particularly challenging because tumours move significantly as the patient breathes, and the scans can be difficult to interpret, noted Boulanger.

“The tumour regions in scan results can be very subtle, and even once identified, need to be tracked over time as the tumour moves with breathing,” he said. “The new algorithm is able to combine many possibilities to find the best descriptors to identify unhealthy regions in a scan.”

The researchers “trained” the neural network on a set of MRI scans in which doctors had identified lung tumours. It then processed an enormous set of images to learn what tumours look like and what properties they share. It was then tested against scans that may or may not contain tumours.

Once the neural network was trained, the researchers put it to the test against another recently developed technique by comparing the two systems with manual tumour identification by an expert radiation oncologist. The new algorithm outperformed the other recent technique in every evaluation measure the researchers used.

Though neural networks could prove critical in identifying tumours, Boulanger noted they will not replace the need for doctors and the importance of human high-level thinking in fully treating patients.

“These tools are designed to improve medical outcomes alongside a skilled professional, and to help to make the process faster,” he said.

“Medicine, as a field, is always looking to go further and improve the quality of care for patients. Neural networks are a tool that can help that goal.”

The new algorithm was developed in collaboration with Nazanin Tahmasebi, a graduate of the Department of Computing Science, and with Kumaradevan Punithakumar and Michelle Noga from the Department of Radiology and Diagnostic Imaging.

The study, “A Fully Convolutional Deep Neural Network for Lung Tumor Boundary Tracking in MRI,” was published in the 40th Annual International Conference Proceedings of the IEEE Engineering in Medicine and Biology.

PreviousNext

SteraMist (Feb)

SteraMist (Feb)

News and Trends

  • RACE streamlines patient journey
  • Healthcare supply chain needs a re-think, observers say
  • EDI spots pricing anomalies in Ontario’s healthcare supply chain
  • AI centres of excellence and companies collaborate on apps
  • Talking Stick: New hope for Indigenous mental healthcare
More from the Print Edition

Subscribe

Subscribe

Free of charge to Canadian hospital managers and executives in nursing homes and home-care organizations. Learn More

Follow us on Social Media!

Follow us on Social Media!

Nihi Data [Winter 2023]

Nihi Data [Winter 2023]

WP

WP

Advertise with us

Advertise with us

Sectra One Cloud

Sectra One Cloud

Change Healthcare [2]

Change Healthcare [2]

Infoway [Feb2023]

Infoway [Feb2023]

Zebra

Zebra

CHT print-200×400

CHT print-200x400

SteraMist (Feb)

SteraMist (Feb)

Advertise with us

Advertise with us

Sectra One Cloud

Sectra One Cloud

Change Healthcare [2]

Change Healthcare [2]

Infoway [Feb2023]

Infoway [Feb2023]

Zebra

Zebra

CHT print-200×400

CHT print-200x400

Contact Us

Canadian Healthcare Technology
1118 Centre Street, Suite 207
Thornhill, Ontario, Canada L4J 7R9
Tel: 905-709-2330
Fax: 905-709-2258
info2@canhealth.com

  • Quick Links
    • Current Print Issue
    • Print Archive
    • Events
    • Vendors
    • About Us
  • Advertise
    • Publishing Schedule
    • Circulation
    • Unit Sizes and Rates
    • Mechanical Requirements
    • Electronic Advertising
    • White Papers
  • Subscribe
    • Print Edition
    • e-Messenger
    • White Papers
  • Resources
    • White Papers
    • Writers’ Guidelines
    • Privacy Policy
  • Topics
    • Administrative Solutions
    • Clinical Solutions
    • Companies
    • Continuing Care
    • Diagnostics
    • Education & Training
  •  
    • Electronic Records
    • Government & Policy
    • Infrastructure
    • Innovation
    • People
    • Privacy and Security

© 2023 Canadian Healthcare Technology

The content of Canadian Healthcare Technology is subject to copyright. Reproduction in whole or in part without prior written permission is strictly prohibited. Send all requests for permission to Jerry Zeidenberg, Publisher.

Search Site

Error: Enter a search term

  • Issues
    • Current Print Issue
    • Print Archive
  • Advertise
    • Publishing Schedule
    • Circulation
    • Unit Sizes and Rates
    • Mechanical Requirements
    • Electronic Advertising
    • White Papers
  • Subscribe
    • Print Edition
    • e-Messenger
    • White Papers
  • Events
  • Vendors
  • About Us