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

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dc.contributor.author Murphy, Keelin
dc.contributor.author van der Aa, Niek E.
dc.contributor.author Negro, Simona
dc.contributor.author Groenendaal, Floris
dc.contributor.author de Vries, Linda S.
dc.contributor.author Viergever, Max A.
dc.contributor.author Boylan, Geraldine B.
dc.contributor.author Benders, Manon
dc.contributor.author Išgum, Ivana
dc.date.accessioned 2017-02-01T12:06:15Z
dc.date.available 2017-02-01T12:06:15Z
dc.date.issued 2017-01-11
dc.identifier.citation Murphy, K., van der Aa, N. E., Negro, S., Groenendaal, F., de Vries, L. S., Viergever, M. A., Boylan, G. B., Benders, M. J. N. L. and Išgum, I. (2017) 'Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy', NeuroImage: Clinical, 14, pp. 222-232. doi:10.1016/j.nicl.2017.01.005 en
dc.identifier.volume 14 en
dc.identifier.startpage 222 en
dc.identifier.endpage 232 en
dc.identifier.issn 2213-1582
dc.identifier.uri http://hdl.handle.net/10468/3550
dc.identifier.doi 10.1016/j.nicl.2017.01.005
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland (SFI grant no. 10/IN.1/B3036 and 12/RC/2272); Irish Research Council (GOIPD/2013/146) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.rights © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Automatic quantification en
dc.subject HIE en
dc.subject MRI en
dc.subject Diffusion-weighted lesions en
dc.subject Segmentation en
dc.subject Neonatal hypoxic ischemic encephalopathy en
dc.title Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Geraldine Boylan, Paediatrics & Child Health, University College Cork, Cork, Ireland. +353-21-490-3000 Email: g.boylan@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2017-02-01T11:55:44Z
dc.description.version Published Version en
dc.internal.rssid 381891479
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Irish Research Council en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Neuroimage: Clinical en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress g.boylan@ucc.ie
dc.internal.IRISemailaddress g.boylan@ucc.ie en


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© 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Except where otherwise noted, this item's license is described as © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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