Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy

Neuroimage Clin. 2017 Jan 11;14:222-232. doi: 10.1016/j.nicl.2017.01.005. eCollection 2017.

Abstract

A fully automatic method for detection and quantification of ischemic lesions in diffusion-weighted MR images of neonatal hypoxic ischemic encephalopathy (HIE) is presented. Ischemic lesions are manually segmented by two independent observers in 1.5 T data from 20 subjects and an automatic algorithm using a random forest classifier is developed and trained on the annotations of observer 1. The algorithm obtains a median sensitivity and specificity of 0.72 and 0.99 respectively. F1-scores are calculated per subject for algorithm performance (median = 0.52) and observer 2 performance (median = 0.56). A paired t-test on the F1-scores shows no statistical difference between the algorithm and observer 2 performances. The method is applied to a larger dataset including 54 additional subjects scanned at both 1.5 T and 3.0 T. The algorithm findings are shown to correspond well with the injury pattern noted by clinicians in both 1.5 T and 3.0 T data and to have a strong relationship with outcome. The results of the automatic method are condensed to a single score for each subject which has significant correlation with an MR score assigned by experienced clinicians (p < 0.0001). This work represents a quantitative method of evaluating diffusion-weighted MR images in neonatal HIE and a first step in the development of an automatic system for more in-depth analysis and prognostication.

Keywords: Automatic quantification; Diffusion-weighted lesions; HIE; MRI; Neonatal hypoxic ischemic encephalopathy; Segmentation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Brain / diagnostic imaging*
  • Brain Ischemia / diagnostic imaging*
  • Brain Mapping*
  • Cohort Studies
  • Diffusion Magnetic Resonance Imaging / methods*
  • False Positive Reactions
  • Female
  • Humans
  • Image Processing, Computer-Assisted*
  • Infant, Newborn
  • Male