Share Email Print
cover

Proceedings Paper • new

Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol
Author(s): M. Bento ; R. Souza; M. Salluzzi; R. Frayne
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Computer-aided diagnosis (CAD) tools using MR images have been largely developed for disease burden quantification, patient diagnosis and follow-up. Newer CAD tools, based on machine learning techniques, often require large and heterogeneous data-sets to provide accurate and generalizable results. Commonly multi-center MR imaging data-sets are used. Typically, collection of these data-sets require adherence to an appropriate experimental protocol in order to assure that findings are due to a pathology and not due to variability in image quality or acquisition parameters across scanners and/or imaging centers. We compared different experimental training protocols used with a representative CAD tool (in this work, designed to identify Alzheimer’s disease (AD) patients from normal control (NC) subjects) using public multi-center data-sets. We examined: 1) subsets of the data-set that were acquired on the same scanner (simulating a single site homogeneous data-set), 2) a traditional cross validation framework (i.e., randomly splitting the data-set into training and testing sets irrespective of centre), and 3) a site-wise cross validation framework, in which training and testing data were differentiated by center using a leave one center out per iteration method. Results achieved with the homogeneous data-set, traditional cross-validation and site-wise cross validation differed (p = 0.0005): 100.0% (i.e., no misclassifications), 99.6% and 97.3% accuracy rates, respectively, even when the same image data-set, features and classifier were used. The lowest accuracy was observed with site-wise cross validation, the only protocol with no site-wise contamination between training and testing samples.

Paper Details

Date Published: 13 March 2019
PDF: 6 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095008 (13 March 2019); doi: 10.1117/12.2512819
Show Author Affiliations
M. Bento , Hotchkiss Brain Institute, Univ. of Calgary (Canada)
Calgary Image Processing and Analysis Ctr. (Canada)
R. Souza, Hotchkiss Brain Institute, Univ. of Calgary (Canada)
Seaman Family MR Research Ctr., Foothills Medical Ctr. (Canada)
M. Salluzzi, Hotchkiss Brain Institute, Univ. of Calgary (Canada)
Calgary Image Processing and Analysis Ctr. (Canada)
R. Frayne, Hotchkiss Brain Institute, Univ. of Calgary (Canada)
Seaman Family MR Research Ctr., Foothills Medical Ctr. (Canada)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

© SPIE. Terms of Use
Back to Top