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
Mar 21, 2006·NeuroImage·Paul A YushkevichGuido Gerig
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
Nov 19, 2011·Radiology·Anwar R PadhaniDavid J Collins
Apr 3, 2012·Medical Image Analysis·Shijun Wang, Ronald M Summers
Jul 29, 2015·Biomedical Engineering Online·Xiaofei SunDefeng Wang
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
May 4, 2017·BMC Cancer·Stuart A TaylorSteve Halligan
May 19, 2017·Journal of Digital Imaging·Marc D KohliJ Raymond Geis
Aug 2, 2017·Medical Physics·Ioannis LavdasAndrea G Rockall
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
Jun 28, 2018·European Radiology·Arash LatifoltojarStuart A Taylor
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
Apr 15, 2021·Pediatric Radiology·Michael M MooreRaymond W Sze
May 1, 2021·Investigative Radiology·Turkay KartSergios Gatidis
Aug 15, 2021·Computer Methods and Programs in Biomedicine·Radhia FerjaouiTarek Kraiem