1. Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy
- Author
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Keelin Murphy, Niek E. van der Aa, Simona Negro, Floris Groenendaal, Linda S. de Vries, Max A. Viergever, Geraldine B. Boylan, Manon J.N.L. Benders, and Ivana Išgum
- Subjects
Automatic quantification ,HIE ,MRI ,Diffusion-weighted lesions ,Segmentation ,Neonatal hypoxic ischemic encephalopathy ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Neurology. Diseases of the nervous system ,RC346-429 - 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.
- Published
- 2017
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