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

Overview of attention for article published in NeuroImage: Clinical, January 2017
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Title
Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy
Published in
NeuroImage: Clinical, January 2017
DOI 10.1016/j.nicl.2017.01.005
Pubmed ID
Authors

Murphy, K, van der Aa, NE, Negro, S, Groenendaal, F, de Vries, LS, Viergever, MA, Boylan, GB, Benders, MJNL, Išgum, I

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Student > Master 9 14%
Student > Bachelor 8 13%
Researcher 6 9%
Other 5 8%
Other 12 19%
Unknown 12 19%
Readers by discipline Count As %
Medicine and Dentistry 20 31%
Computer Science 6 9%
Neuroscience 5 8%
Nursing and Health Professions 4 6%
Psychology 4 6%
Other 6 9%
Unknown 19 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 May 2017.
All research outputs
#13,722,642
of 15,553,472 outputs
Outputs from NeuroImage: Clinical
#1,656
of 1,839 outputs
Outputs of similar age
#228,821
of 269,796 outputs
Outputs of similar age from NeuroImage: Clinical
#10
of 12 outputs
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