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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two methods of breast segmentation were applied to the task of calculating<jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an<jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>component. A semiautomated, bias\u2010corrected, fuzzy C\u2010means (<jats:styled-content style=\"fixed-case\">BC<\/jats:styled-content>\u2010<jats:styled-content style=\"fixed-case\">FCM<\/jats:styled-content>) method was combined with morphological operations to segment the overall breast volume from in\u2010phase Dixon images. The method makes use of novel, problem\u2010specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon<jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>density values. Contemporaneously acquired T<jats:sub>1<\/jats:sub>\u2010 and T<jats:sub>2<\/jats:sub>\u2010weighted 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\u2010water discrimination was performed using an Expectation Maximization\u2013Markov Random Field technique, yielding a second independent estimate of<jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>density.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject\u2010specific. Dice and Jaccard coefficients comparing the semiautomated<jats:styled-content style=\"fixed-case\">BC<\/jats:styled-content>\u2010<jats:styled-content style=\"fixed-case\">FCM<\/jats:styled-content>method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T<jats:sub>1<\/jats:sub>\u2010 and T<jats:sub>2<\/jats:sub>\u2010weighted 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.12320","type":"journal-article","created":{"date-parts":[[2017,5,6]],"date-time":"2017-05-06T08:04:36Z","timestamp":1494057876000},"page":"4573-4592","source":"Crossref","is-referenced-by-count":22,"title":["Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?"],"prefix":"10.1002","volume":"44","author":[{"given":"Simon J.","family":"Doran","sequence":"first","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research Cancer Research UK Cancer Imaging Centre London SM2 5NG UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John H.","family":"Hipwell","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Bioengineering UCL, Centre for Medical Image Computing (CMIC) London WC1E 7JE UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Denholm","sequence":"additional","affiliation":[{"name":"Department of Non\u2010Communicable Disease Epidemiology London School of Hygiene &amp; Tropical Medicine London WC1E 7HT UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f6rn","family":"Eiben","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Bioengineering UCL, Centre for Medical Image Computing (CMIC) London WC1E 7JE UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Busana","sequence":"additional","affiliation":[{"name":"Department of Non\u2010Communicable Disease Epidemiology London School of Hygiene &amp; Tropical Medicine London WC1E 7HT UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David J.","family":"Hawkes","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Bioengineering UCL, Centre for Medical Image Computing (CMIC) London WC1E 7JE UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin O.","family":"Leach","sequence":"additional","affiliation":[{"name":"Division of Radiotherapy and Imaging, The Institute of Cancer Research Cancer Research UK Cancer Imaging Centre London SM2 5NG UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isabel dos Santos","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Non\u2010Communicable Disease Epidemiology London School of Hygiene &amp; Tropical Medicine London WC1E 7HT UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,7,25]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-06-0034"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0086858"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.15.14668"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2004.12.004"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13666-5_46"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4948503"},{"key":"e_1_2_8_8_1","first-page":"2870","article-title":"Volumetric breast density estimation from full\u2010field digital mammograms: a validation study","volume":"43","author":"Gubern\u2010M\u00e9rida A","year":"2014","journal-title":"PLoS One"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/bcr2447"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(97)85011-6"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.854517"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.594522"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.594671"},{"key":"e_1_2_8_14_1","unstructured":"LuWLW YaoJYJ LuCLC PrindivilleS ChowC.DCE\u2010MRI segmentation and motion correction based on active contour model and forward mapping. 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