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dc.contributor.authorNahas, Kamal
dc.contributor.authorFerreira Fernandes, João
dc.contributor.authorVyas, Nina
dc.contributor.authorCrump, Colin
dc.contributor.authorGraham, Stephen
dc.contributor.authorHarkiolaki, Maria
dc.date.accessioned2022-04-13T23:30:51Z
dc.date.available2022-04-13T23:30:51Z
dc.date.issued2022
dc.identifier.issn2633-903X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336089
dc.description.abstractCryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.
dc.description.sponsorshipDiamond Light Source Ltd.
dc.publisherCambridge University Press
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleContour, a semi-automated segmentation and quantitation tool for cryo-soft-X-ray tomography
dc.typeArticle
dc.publisher.departmentDepartment of Pathology Student
dc.date.updated2022-04-13T14:29:16Z
prism.publicationNameBiological Imaging
dc.identifier.doi10.17863/CAM.83518
dcterms.dateAccepted2022-04-04
rioxxterms.versionofrecord10.1017/S2633903X22000046
rioxxterms.versionVoR
dc.contributor.orcidNahas, Kamal [0000-0003-3501-8473]
dc.contributor.orcidCrump, Colin [0000-0001-9918-9998]
dc.contributor.orcidGraham, Stephen [0000-0003-4547-4034]
dc.identifier.eissn2633-903X
dc.publisher.urlhttps://www.cambridge.org/core/journals/biological-imaging/article/contour-a-semiautomated-segmentation-and-quantitation-tool-for-cryosoftxray-tomography/35B858F60A8B28468F4A2248BA0C7247
rioxxterms.typeJournal Article/Review
pubs.funder-project-idBiotechnology and Biological Sciences Research Council (BB/M021424/1)
pubs.funder-project-idWellcome Trust (098406/Z/12/B)
pubs.funder-project-idWellcome Trust (098406/Z/12/Z)
cam.issuedOnline2022-05-17
cam.depositDate2022-04-13
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International