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dc.contributor.authorDoran, SJen_US
dc.contributor.authorHipwell, JHen_US
dc.contributor.authorDenholm, Ren_US
dc.contributor.authorEiben, Ben_US
dc.contributor.authorBusana, Men_US
dc.contributor.authorHawkes, DJen_US
dc.contributor.authorLeach, MOen_US
dc.contributor.authorSilva, IDSen_US
dc.date.accessioned2017-05-23T15:12:24Z
dc.date.issued2017-09en_US
dc.identifier.citationMedical physics, 2017, 44 (9), pp. 4573 - 4592en_US
dc.identifier.issn0094-2405en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/646
dc.identifier.eissn2473-4209en_US
dc.identifier.doi10.1002/mp.12320en_US
dc.description.abstractPURPOSE:To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection. METHODS:Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density. RESULTS:Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue. CONCLUSIONS:Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient.en_US
dc.formatPrint-Electronicen_US
dc.format.extent4573 - 4592en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_US
dc.subjectBreasten_US
dc.subjectHumansen_US
dc.subjectMagnetic Resonance Imagingen_US
dc.subjectRadiographyen_US
dc.subjectLongitudinal Studiesen_US
dc.subjectAlgorithmsen_US
dc.subjectFemaleen_US
dc.titleBreast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?en_US
dc.typeJournal Article
dcterms.dateAccepted2017-04-03en_US
rioxxterms.versionofrecord10.1002/mp.12320en_US
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0en_US
rioxxterms.licenseref.startdate2017-09en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfMedical physicsen_US
pubs.issue9en_US
pubs.notesNo embargoen_US
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Magnetic Resonance
pubs.publication-statusPublisheden_US
pubs.volume44en_US
pubs.embargo.termsNo embargoen_US
icr.researchteamMagnetic Resonanceen_US
dc.contributor.icrauthorLeach, Martinen_US
dc.contributor.icrauthorDoran, Simonen_US


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