Title |
Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy
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Published in |
NeuroImage: Clinical, January 2017
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DOI | 10.1016/j.nicl.2017.01.005 |
Pubmed ID | |
Authors |
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, Ivana Išgum |
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. |
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Unknown | 1 | 100% |
Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 70 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 13 | 19% |
Student > Master | 8 | 11% |
Student > Bachelor | 7 | 10% |
Researcher | 6 | 9% |
Other | 5 | 7% |
Other | 12 | 17% |
Unknown | 19 | 27% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 18 | 26% |
Computer Science | 6 | 9% |
Psychology | 5 | 7% |
Neuroscience | 5 | 7% |
Nursing and Health Professions | 4 | 6% |
Other | 6 | 9% |
Unknown | 26 | 37% |