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Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Shao, Muhan
dc.contributor.author Han, Shuo
dc.contributor.author Carass, Aaron
dc.contributor.author Li, Xiang
dc.contributor.author Blitz, Ari M.
dc.contributor.author Shin, Jaehoon
dc.contributor.author Prince, Jerry L.
dc.contributor.author Ellingsen, Lotta María
dc.date.accessioned 2020-09-09T11:36:30Z
dc.date.available 2020-09-09T11:36:30Z
dc.date.issued 2019
dc.identifier.citation Shao, M., et al. (2019). "Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly." NeuroImage: Clinical 23: 101871.
dc.identifier.issn 2213-1582
dc.identifier.uri https://hdl.handle.net/20.500.11815/2054
dc.description Publisher's version (útgefin grein)
dc.description.abstract Numerous brain disorders are associated with ventriculomegaly, including both neuro-degenerative diseases and cerebrospinal fluid disorders. Detailed evaluation of the ventricular system is important for these conditions to help understand the pathogenesis of ventricular enlargement and elucidate novel patterns of ventriculomegaly that can be associated with different diseases. One such disease is normal pressure hydrocephalus (NPH), a chronic form of hydrocephalus in older adults that causes dementia. Automatic parcellation of the ventricular system into its sub-compartments in patients with ventriculomegaly is quite challenging due to the large variation of the ventricle shape and size. Conventional brain labeling methods are time-consuming and often fail to identify the boundaries of the enlarged ventricles. We propose a modified 3D U-Net method to perform accurate ventricular parcellation, even with grossly enlarged ventricles, from magnetic resonance images (MRIs). We validated our method on a data set of healthy controls as well as a cohort of 95 patients with NPH with mild to severe ventriculomegaly and compared with several state-of-the-art segmentation methods. On the healthy data set, the proposed network achieved mean Dice similarity coefficient (DSC) of 0.895 ± 0.03 for the ventricular system. On the NPH data set, we achieved mean DSC of 0.973 ± 0.02, which is significantly (p < 0.005) higher than four state-of-the-art segmentation methods we compared with. Furthermore, the typical processing time on CPU-base implementation of the proposed method is 2 min, which is much lower than the several hours required by the other methods. Results indicate that our method provides: 1) highly robust parcellation of the ventricular system that is comparable in accuracy to state-of-the-art methods on healthy controls; 2) greater robustness and significantly more accurate results on cases of ventricular enlargement; and 3) a tool that enables computation of novel imaging biomarkers for dilated ventricular spaces that characterize the ventricular system. © 2019 The Authors
dc.description.sponsorship This work was supported by the NIH/NINDS under grant R21-NS096497 . Support was also provided by the National Multiple Sclerosis Society grant RG-1507-05243 , the Department of Defense in the Center for Neuroscience and Regenerative Medicine , and the Icelandic Centre for Research (RANNIS) under grant 173942051 . The author Shuo Han is in part supported by the Intramural Research Program of the NIH , National Institute on Aging . This research project was conducted using computational resources at the Maryland Advanced Research Computing Center (MARCC).
dc.format.extent 101871
dc.language.iso en
dc.publisher Elsevier BV
dc.relation.ispartofseries NeuroImage: Clinical;23(2019)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Convolutional neural networks
dc.subject Enlarged brain ventricles
dc.subject Labeling
dc.subject Magnetic resonance imaging
dc.subject Normal pressure hydrocephalus
dc.subject Ventricular system
dc.subject Heilabilun
dc.subject Taugakerfi
dc.subject Myndgreining (upplýsingatækni)
dc.title Brain ventricle parcellation using a deep neural network: Application to patients with ventriculomegaly
dc.type info:eu-repo/semantics/article
dcterms.license Open access. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
dc.description.version Peer Reviewed
dc.identifier.journal NeuroImage: Clinical
dc.identifier.doi 10.1016/j.nicl.2019.101871
dc.relation.url https://www.sciencedirect.com/science/article/pii/S2213158219302219?via%3Dihub
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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