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

Overview

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).

publication date

  • March 8, 2018

Research

keywords

  • Algorithms
  • Image Interpretation, Computer-Assisted
  • Magnetic Resonance Imaging
  • Multiple Sclerosis

Identity

PubMed Central ID

  • PMC5955764

Scopus Document Identifier

  • 85047086778

Digital Object Identifier (DOI)

  • 10.1016/S1474-4422(15)00393-2

PubMed ID

  • 29519791

Additional Document Info

volume

  • 39

issue

  • 5