{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T09:07:38Z","timestamp":1792919258919,"version":"4.5.0"},"reference-count":41,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T00:00:00Z","timestamp":1670544000000},"content-version":"vor","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":["11774158"],"award-info":[{"award-number":["11774158"]}],"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":["11934008"],"award-info":[{"award-number":["11934008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Breast ultrasound (BUS) imaging is one of the most prevalent approaches for the detection of breast cancers. Tumor segmentation of BUS images can facilitate doctors in localizing tumors and is a necessary step for computer\u2010aided diagnosis systems. While the majority of clinical BUS scans are normal ones without tumors, segmentation approaches such as U\u2010Net often predict mass regions for these images. Such false\u2010positive problem becomes serious if a fully automatic artificial intelligence system is used for routine screening.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we proposed a novel model which is more suitable for routine BUS screening. The model contains a classification branch that determines whether the image is normal or with tumors, and a segmentation branch that outlines tumors. Two branches share the same encoder network. We also built a new dataset that contains 1600 BUS images from 625 patients for training and a testing dataset with 130 images from 120 patients for testing. The dataset is the largest one with pixel\u2010wise masks manually segmented by experienced radiologists. Our code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/szhangNJU\/BUS_segmentation\">https:\/\/github.com\/szhangNJU\/BUS_segmentation<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The area under the receiver operating characteristic curve (AUC) for classifying images into normal\/abnormal categories was 0.991. The dice similarity coefficient (DSC) for segmentation of mass regions was 0.898, better than the state\u2010of\u2010the\u2010art models. Testing on an external dataset gave a similar performance, demonstrating a good transferability of our model. Moreover, we simulated the use of the model in actual clinic practice by processing videos recorded during BUS scans; the model gave very low false\u2010positive predictions on normal images without sacrificing sensitivities for images with tumors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our model achieved better segmentation performance than the state\u2010of\u2010the\u2010art models and showed a good transferability on an external test set. The proposed deep learning architecture holds potential for use in fully automatic BUS health screening.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13863","type":"journal-article","created":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T06:51:10Z","timestamp":1670655070000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Fully automatic tumor segmentation of breast ultrasound images with deep learning"],"prefix":"10.1002","volume":"24","author":[{"given":"Shuai","family":"Zhang","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center of Advanced Microstructures School of Physics Nanjing University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Third Affiliated Hospital Sun Yat\u2010sen University  Guangzhou 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