{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T01:11:12Z","timestamp":1787793072078,"version":"build-2784847793"},"reference-count":47,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T00:00:00Z","timestamp":1772496000000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Numerous neural networks based on the U\u2010Net architecture have been developed for the segmentation of breast ultrasound images. However, the accuracy of such segmentation tasks is often compromised by the complex and variable shapes of tumors, the indistinct boundaries of lesion areas, and background\u00a0noise.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>In this study, we develop a hybrid receptive field U\u2010Net (HRF U\u2010Net) to improve lesion segmentation in breast ultrasound\u00a0images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We design an innovative hybrid receptive field module (HRFM) to replace conventional convolutional layers, integrating deformable convolutions that expand the receptive field and enhance the model's ability to capture shape and boundary features. We systematically analyze the differential feature extraction capabilities of deformable convolutions across network layers, combining them with standard convolutions, dilated convolutions, and max pooling. This configuration enables HRF U\u2010Net to achieve a broader and more adaptive receptive field, enhancing its effectiveness in handling the complexities of breast lesion segmentation. Additionally, we introduce a large\u2010kernel attention module (LKAM) within the skip connections, which expands the receptive field and supports adaptive feature selection, capturing long\u2010range dependencies within the convolutional attention mechanism. This novel approach enables more precise feature extraction, effectively mitigates boundary noise during training, and substantially improves the model's segmentation performance. We used three publicly available datasets to conduct extensive experiments, including ablation studies, comparative analyses, robustness evaluations, and external validation. Datasets A and B were divided into training and validation sets for four\u2010fold cross\u2010validation, while Dataset C was used as the test set for external validation. We selected five widely used image segmentation metrics for validation, namely pixel accuracy, precision, recall, Jaccard index, and Dice coefficient. In addition, the statistical significance was evaluated using the paired Student's\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test with Holm\u2013Bonferroni correction .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Experimental results on three public datasets demonstrate that the proposed HRF U\u2010Net substantially improves the efficacy of breast ultrasound image segmentation, outperforming several state\u2010of\u2010the\u2010art works. Specifically, in the comparative experiments, HRF U\u2010Net achieved scores of 96.52, 85.03, 83.19, 73.15, and 81.34  for the five metrics on Dataset A. On Dataset B, the scores reached 98.61, 90.22, 83.99, 74.50, and 83.64 . The effect size Cohen's\n                      <jats:italic>d<\/jats:italic>\n                      values for HRF U\u2010Net compared to several typical and advanced networks all exceed 0.32, demonstrating a significant improvement in segmentation performance. In external validation, the metric scores reached 97.02, 84.35, 93.94, 77.74, and 86.86 .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The HRF U\u2010Net proposed in this paper improves the segmentation accuracy of breast ultrasound\u00a0images.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70370","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:36:47Z","timestamp":1772962607000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid receptive field U\u2010Net for lesion segmentation in breast ultrasound images"],"prefix":"10.1002","volume":"53","author":[{"given":"Zhengbo","family":"Xue","sequence":"first","affiliation":[{"name":"Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming Yunnan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Feng","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming Yunnan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nianbo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering University of Electronic Science and Technology of China Quzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,3]]},"reference":[{"issue":"4","key":"e_1_2_9_2_1","article-title":"Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review","volume":"16","author":"Rentiya ZS","year":"2024","journal-title":"Cureus"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000036905"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.02.012"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4921612"},{"key":"e_1_2_9_6_1","doi-asserted-by":"crossref","unstructured":"DhungelN CarneiroG BradleyAP.Fully automated classification of mammograms using deep residual neural networks. 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