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dc.contributor.authorDoran, SJ
dc.contributor.authorHipwell, JH
dc.contributor.authorDenholm, R
dc.contributor.authorEiben, B
dc.contributor.authorBusana, M
dc.contributor.authorHawkes, DJ
dc.contributor.authorLeach, MO
dc.contributor.authorSilva, IDS
dc.date.accessioned2017-05-23T15:12:24Z
dc.date.issued2017-09
dc.identifier.citationMedical physics, 2017, 44 (9), pp. 4573 - 4592
dc.identifier.issn0094-2405
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/646
dc.identifier.eissn2473-4209
dc.identifier.doi10.1002/mp.12320
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 T 1 - and T 2 -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 T 1 - and T 2 -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.
dc.formatPrint-Electronic
dc.format.extent4573 - 4592
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved
dc.subjectBreast
dc.subjectHumans
dc.subjectMagnetic Resonance Imaging
dc.subjectRadiography
dc.subjectLongitudinal Studies
dc.subjectAlgorithms
dc.subjectFemale
dc.titleBreast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?
dc.typeJournal Article
dcterms.dateAccepted2017-04-03
rioxxterms.versionofrecord10.1002/mp.12320
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2017-09
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfMedical physics
pubs.issue9
pubs.notesNo embargo
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.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-statusPublished
pubs.volume44
pubs.embargo.termsNo embargo
icr.researchteamMagnetic Resonanceen_US
dc.contributor.icrauthorLeach, Martinen
dc.contributor.icrauthorDoran, Simonen


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