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 |