{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:26:12Z","timestamp":1792787172746,"version":"4.5.0"},"reference-count":28,"publisher":"Wiley","issue":"8Part1","license":[{"start":{"date-parts":[[2014,7,23]],"date-time":"2014-07-23T00:00:00Z","timestamp":1406073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":405,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NNSFC","doi-asserted-by":"publisher","award":["61003175"],"award-info":[{"award-number":["61003175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NNSFC","doi-asserted-by":"publisher","award":["81071127"],"award-info":[{"award-number":["81071127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2012BAJ18B06"],"award-info":[{"award-number":["2012BAJ18B06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2012BAJ18B06"],"award-info":[{"award-number":["2012BAJ18B06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NNSFC","award":["81071127"],"award-info":[{"award-number":["81071127"]}]},{"name":"NNSFC","award":["61003175"],"award-info":[{"award-number":["61003175"]}]}],"content-domain":{"domain":["aip.org"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2014,8]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three\u2010dimensional lesions from dynamic contrast\u2010enhanced magnetic resonance images (DCE\u2010MRIs) of the breast.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The authors propose a new background distribution\u2010based active contour model using level set (BDACMLS) to segment lesions in breast DCE\u2010MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors\u2019 method. Finally, the performance of the proposed method is evaluated by several region\u2010based metrics such as the overlap ratio.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Forty\u2010two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinoma\n                      <jats:italic>in situ<\/jats:italic>\n                      , scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% \u00b1 12.60% (mean \u00b1 s.d.).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>A new active contour model method has been developed and shown to successfully segment breast DCE\u2010MRI three\u2010dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak\u2010edge\u2010passed problem, and improve the robustness in segmenting different lesions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1118\/1.4886295","type":"journal-article","created":{"date-parts":[[2014,7,23]],"date-time":"2014-07-23T20:32:00Z","timestamp":1406147520000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":13,"title":["A new background distribution\u2010based active contour model for three\u2010dimensional lesion segmentation in breast DCE\u2010MRI"],"prefix":"10.1002","volume":"41","author":[{"given":"Hui","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuowei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianshuang","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2014,7,23]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2009.12.014"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21166"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3151811"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3691178"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.20541"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.22680"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1695652"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.20794"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2005.08.035"},{"key":"e_1_2_6_11_1","doi-asserted-by":"crossref","first-page":"156","DOI":"10.4103\/2228-7477.95284","article-title":"A new Markov random field segmentation method for breast lesion segmentation in MR images","volume":"1","author":"Azmi R.","year":"2011","journal-title":"J. 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