Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data

Clinical Radiology
I LavdasA G Rockall

Abstract

Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project, which aims to develop machine learning methods to improve the diagnostic performance and reduce the radiology reading time of whole-body magnetic resonance imaging (MRI) scans, in patients being staged for cancer (MALIBO study). We describe here the main challenges we have encountered during the course of this project. Data quality and uniformity are the two most important data traits to be considered in clinical trials incorporating machine learning. Robust data pre-processing and machine learning pipelines have been employed in MALIBO, a task facilitated by the now freely available machine learning libraries and toolboxes. Another important consideration for achieving the desired clinical outcome in MALIBO, was to effectively host the resulting machine learning output, along with the clinical images, for reading in a clinical environment. Finally, a range of legal, ethical, and clinical acceptance issues should be considered when attempting to incorporate computer-assisting tools...Continue Reading

References

Oct 26, 1999·IEEE Transactions on Medical Imaging·D RueckertD J Hawkes
Nov 26, 1999·Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine·L G Nyúl, J K Udupa
Apr 28, 2000·IEEE Transactions on Medical Imaging·L G NyúlX Zhang
Aug 10, 2006·Journal of Magnetic Resonance Imaging : JMRI·Denis Le BihanFranck Lethimonnier
Oct 7, 2006·Medical Physics·Anant Madabhushi, Jayaram K Udupa
May 23, 2007·AJR. American Journal of Roentgenology·Dow-Mu Koh, David J Collins
Feb 13, 2009·IEEE Transactions on Medical Imaging·Tobias HeimannIvo Wolf
May 22, 2009·European Journal of Radiology·Gerwin P SchmidtAndrea Baur-Melnyk
May 28, 2011·Journal of Magnetic Resonance Imaging : JMRI·Lian-Ming WuJian-Rong Xu
Apr 3, 2012·Medical Image Analysis·Shijun Wang, Ronald M Summers
Nov 20, 2016·Medical Image Analysis·Konstantinos KamnitsasBen Glocker
Jan 27, 2017·AJR. American Journal of Roentgenology·Marc KohliJ Raymond Geis
Feb 18, 2017·Radiographics : a Review Publication of the Radiological Society of North America, Inc·Bradley J EricksonTimothy L Kline
Nov 14, 2017·Radiographics : a Review Publication of the Radiological Society of North America, Inc·Gabriel ChartrandAn Tang
Feb 6, 2018·Journal of the American College of Radiology : JACR·Bradley J EricksonAlexander D Weston

❮ Previous
Next ❯

Citations

Jan 1, 2021·Journal of Magnetic Resonance Imaging : JMRI·Sandy Van NieuwenhoveFrederic E Lecouvet
Jun 9, 2020·Ageing Research Reviews·Brenna OsborneMorten Scheibye-Knudsen

❮ Previous
Next ❯

Related Concepts

Related Feeds

Cancer Imaging

Imaging techniques, including CT and MR, have become essential to tumor detection, diagnosis, and monitoring. Here is the latest research on cancer imaging.