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dc.contributor.authorInglis, Andrewen_US
dc.contributor.authorCruz, Luisen_US
dc.contributor.authorRoe, Dan L.en_US
dc.contributor.authorStanley, Harry Eugeneen_US
dc.contributor.authorRosene, Douglas L.en_US
dc.contributor.authorUrbanc, Brigitaen_US
dc.date.accessioned2020-04-02T15:57:08Z
dc.date.available2020-04-02T15:57:08Z
dc.date.issued2008-06-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000256161300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationA. Inglis, L. Cruz, D.L. Roe, H.E. Stanley, D.L. Rosene, B. Urbanc. 2008. "Automated identification of neurons and their locations." JOURNAL OF MICROSCOPY, Volume 230, Issue 3, pp. 339 - 352 (14). https://doi.org/10.1111/j.1365-2818.2008.01992.x
dc.identifier.issn0022-2720
dc.identifier.urihttps://hdl.handle.net/2144/39938
dc.description"This is the peer reviewed version of the following article: A. Inglis, L. Cruz, D.L. Roe, H.E. Stanley, D.L. Rosene, B. Urbanc. 2008. "Automated identification of neurons and their locations." JOURNAL OF MICROSCOPY, Volume 230, Issue 3, pp. 339 - 352, which has been published in final form at https://doi.org/10.1111/j.1365-2818.2008.01992.x. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions."en_US
dc.description.abstractIndividual locations of many neuronal cell bodies (>10^4) are needed to enable statistically significant measurements of spatial organization within the brain such as nearest‐neighbour and microcolumnarity measurements. In this paper, we introduce an Automated Neuron Recognition Algorithm (ANRA) which obtains the (x, y) location of individual neurons within digitized images of Nissl‐stained, 30 μm thick, frozen sections of the cerebral cortex of the Rhesus monkey. Identification of neurons within such Nissl‐stained sections is inherently difficult due to the variability in neuron staining, the overlap of neurons, the presence of partial or damaged neurons at tissue surfaces, and the presence of non‐neuron objects, such as glial cells, blood vessels, and random artefacts. To overcome these challenges and identify neurons, ANRA applies a combination of image segmentation and machine learning. The steps involve active contour segmentation to find outlines of potential neuron cell bodies followed by artificial neural network training using the segmentation properties (size, optical density, gyration, etc.) to distinguish between neuron and non‐neuron segmentations. ANRA positively identifies 86 ± 5% neurons with 15 ± 8% error (mean ± SD) on a wide range of Nissl‐stained images, whereas semi‐automatic methods obtain 80 ± 7%/17 ± 12%. A further advantage of ANRA is that it affords an unlimited increase in speed from semi‐automatic methods, and is computationally efficient, with the ability to recognize ∼100 neurons per minute using a standard personal computer. ANRA is amenable to analysis of huge photo‐montages of Nissl‐stained tissue, thereby opening the door to fast, efficient and quantitative analysis of vast stores of archival material that exist in laboratories and research collections around the world.en_US
dc.description.sponsorshipR01 AG021133-01A2 - NIA NIH HHS; R01 AG021133 - NIA NIH HHS; R01 AG021133-04 - NIA NIH HHS; R03 AG024633-01 - NIA NIH HHS; R01-AG021133 - NIA NIH HHS; R01 AG021133-02 - NIA NIH HHS; P01 AG000001 - NIA NIH HHS; R03 AG024633-02 - NIA NIH HHS; R01 AG021133-03 - NIA NIH HHS; P01-AG00001 - NIA NIH HHS; P51 RR000165 - NCRR NIH HHS; P51-RR00165 - NCRR NIH HHSen_US
dc.format.extentp. 339 - 352en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherWILEY-BLACKWELLen_US
dc.relation.ispartofJOURNAL OF MICROSCOPY
dc.subjectScience & technologyen_US
dc.subjectMicroscopyen_US
dc.subjectActive contoursen_US
dc.subjectArtificial neural networken_US
dc.subjectNeuron identificationen_US
dc.subjectNissl-stainen_US
dc.subjectTrainingen_US
dc.subjectConfocal microscope imagesen_US
dc.subjectSpatial-distributionen_US
dc.subjectTissue-sectionsen_US
dc.subjectAnalysis systemen_US
dc.subjectCell-nucleien_US
dc.subjectSegmentationen_US
dc.subjectBrainen_US
dc.subjectStereologyen_US
dc.subjectCortexen_US
dc.subjectAlgorithmsen_US
dc.subjectAnimalsen_US
dc.subjectAutomationen_US
dc.subjectCell nucleolusen_US
dc.subjectCell nucleusen_US
dc.subjectImage processing, computer-assisteden_US
dc.subjectMacaca mulattaen_US
dc.subjectNeuronsen_US
dc.subjectNissl bodiesen_US
dc.subjectCondensed matter physicsen_US
dc.subjectMaterials engineeringen_US
dc.subjectBiochemistry and cell biologyen_US
dc.titleAutomated identification of neurons and their locationsen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1111/j.1365-2818.2008.01992.x
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Physicsen_US
pubs.organisational-groupBoston University, School of Medicineen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv57893


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