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dc.contributor.authorJanjic, Predragen_US
dc.contributor.authorPetrovski, Kristijanen_US
dc.contributor.authorDolgoski, Blagojaen_US
dc.contributor.authorSmiley, Johnen_US
dc.contributor.authorZdravkovski, Pancheen_US
dc.contributor.authorPavlovski, Goranen_US
dc.contributor.authorJakjovski, Zlatkoen_US
dc.contributor.authorDavcheva, Natashaen_US
dc.contributor.authorPoposka, Vericaen_US
dc.contributor.authorStankov, Aleksandaren_US
dc.contributor.authorRosoklija, Gorazden_US
dc.contributor.authorPetrushevska, Gordanaen_US
dc.contributor.authorKocarev, Ljupcoen_US
dc.contributor.authorDwork, Andrew Jen_US
dc.description.abstractStandard segmentation of high-contrast electron micrographs (EM) identifies myelin accurately but does not translate easily into measurements of individual axons and their myelin, even in cross-sections of parallel fibers. We describe automated segmentation and measurement of each myelinated axon and its sheath in EMs of arbitrarily oriented human white matter from autopsies.en_US
dc.publisherElsevier BVen_US
dc.relation.ispartofJournal of Neuroscience Methodsen_US
dc.titleMeasurement-oriented deep-learning workflow for improved segmentation of myelin and axons in high-resolution images of human cerebral white matteren_US
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item.grantfulltextnone- of Medicine- of Medicine- of Medicine- of Medicine- of Medicine- of Medicine- of Medicine-
Appears in Collections:Faculty of Medicine: Journal Articles
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