{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:36:47Z","timestamp":1788226607805,"version":"build-2803163510"},"reference-count":46,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Breast ultrasound (BUS) is an important breast imaging tool. Automatic BUS image segmentation can measure the breast tumor size objectively and reduce doctors\u2019 workload. In this article, we proposed a deep supervised transformer U\u2010shaped full\u2010resolution residual network (DSTransUFRRN) to segment BUS images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In the proposed method, a full\u2010resolution residual stream and a deep supervision mechanism were introduced into TransU\u2010Net. The residual stream can keep full resolution features from different levels and enhance features fusion. Then, the deep supervision can suppress gradient dispersion. Moreover, the transformer module can suppress irrelevant features and improve feature extraction process. Two datasets (dataset A and B) were used for training and evaluation. The dataset A included 980 BUS image samples and the dataset B had 163 BUS image samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Cross\u2010validation was conducted. For the dataset A, the proposed DSTransUFRRN achieved significantly higher Dice (91.04\u00a0\u00b1\u00a00.86%) than all compared methods (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05). For the dataset B, the Dice was lower than that for the dataset A due to the small number of samples, but the Dice of DSTransUFRRN (88.15%\u00a0\u00b1\u00a02.11%) was significantly higher than that of other compared methods (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>In this study, we proposed DSTransUFRRN for BUS image segmentation. The proposed methods achieved significantly higher accuracy than the compared previous methods.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16765","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T04:15:45Z","timestamp":1697084145000},"page":"7513-7524","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A deep supervised transformer U\u2010shaped full\u2010resolution residual network for the segmentation of breast ultrasound image"],"prefix":"10.1002","volume":"50","author":[{"given":"Jiale","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronics Engineering Beihang University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuoxun","family":"Hou","sequence":"additional","affiliation":[{"name":"Beijing Institute of Mechanics &amp; Electricity  Beijing 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