Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm.

Publication Type	Academic Article
Authors	Nguyen T, Zhang S, Gupta A, Zhao Y, Gauthier S, Wang Y
Journal	AJNR Am J Neuroradiol
Volume	39
Issue	5
Pagination	830-833
Date Published	03/08/2018
ISSN	1936-959X
Keywords	Algorithms, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Multiple Sclerosis
Abstract	We developed a robust automated algorithm called statistical detection of changes for detecting morphologic changes of multiple sclerosis lesions between 2 T2-weighted FLAIR brain images. Results from 30 patients showed that statistical detection of changes achieved significantly higher sensitivity and specificity (0.964, 95% CI, 0.823-0.994; 0.691, 95% CI, 0.612-0.761) than with the lesion-prediction algorithm (0.614, 95% CI, 0.410-0.784; 0.281, 95% CI, 0.228-0.314), while resulting in a 49% reduction in human review time (P = .007).
DOI	10.3174/ajnr.A5594
PubMed ID	29519791
PubMed Central ID	PMC5955764