{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T09:01:58Z","timestamp":1768208518858,"version":"3.49.0"},"reference-count":27,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T00:00:00Z","timestamp":1580169600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602419"],"award-info":[{"award-number":["61602419"]}],"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":["LY16F010008"],"award-info":[{"award-number":["LY16F010008"]}],"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":["LQ16F020003"],"award-info":[{"award-number":["LQ16F020003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["61602419"],"award-info":[{"award-number":["61602419"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY16F010008"],"award-info":[{"award-number":["LY16F010008"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ16F020003"],"award-info":[{"award-number":["LQ16F020003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["61602419"],"award-info":[{"award-number":["61602419"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY16F010008"],"award-info":[{"award-number":["LY16F010008"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ16F020003"],"award-info":[{"award-number":["LQ16F020003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2020,1,28]]},"abstract":"<jats:p>To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.<\/jats:p>","DOI":"10.1155\/2020\/7156165","type":"journal-article","created":{"date-parts":[[2020,1,28]],"date-time":"2020-01-28T23:40:17Z","timestamp":1580254817000},"page":"1-10","source":"Crossref","is-referenced-by-count":18,"title":["DBT Masses Automatic Segmentation Using U-Net Neural Networks"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1305-5057","authenticated-orcid":true,"given":"Xiaobo","family":"Lai","sequence":"first","affiliation":[{"name":"College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1262-4887","authenticated-orcid":true,"given":"Weiji","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Life Science, Zhejiang Chinese Medical University, Hangzhou 310053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7335-2742","authenticated-orcid":true,"given":"Ruipeng","family":"Li","sequence":"additional","affiliation":[{"name":"Hangzhou Third People\u2019s Hospital, Hangzhou 310009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.08.072"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1520\/jte20180504"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3855-9"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190345"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019182627"},{"key":"6","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.rcl.2010.06.009","volume":"48","year":"2010","journal-title":"Radiologic Clinics of North America"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019190132"},{"key":"8","first-page":"616","volume":"189","year":"2012","journal-title":"American Journal of Roentgenology"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1177\/0284185118815315"},{"key":"10","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1002\/mp.13534","volume":"46","year":"2019","journal-title":"Medical Physics"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2018.06.019"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2019.01.019"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2018.2870343"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1177\/153303460400300504"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2373041657"},{"key":"16","volume-title":"Detection of masses in digital breast tomosynthesis mammography: effects of the number of projection views and dose","volume":"5116","year":"2008"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1118\/1.4791643"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.01.009"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/17\/5003"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112829"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1186\/s42490-019-0003-2"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.01.012"},{"key":"27","doi-asserted-by":"crossref","first-page":"171","DOI":"10.31083\/j.rcm.2019.03.5201","volume":"20","year":"2019","journal-title":"Reviews in Cardiovascular Medicine"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1109\/jstars.2019.2925841"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.12.003"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1118\/1.4967345"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1118\/1.2163390"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2020\/7156165.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2020\/7156165.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2020\/7156165.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T21:00:25Z","timestamp":1695675625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2020\/7156165\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,28]]},"references-count":27,"alternative-id":["7156165","7156165"],"URL":"https:\/\/doi.org\/10.1155\/2020\/7156165","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,28]]}}}