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To address these challenges, this study proposes a segmentation method specifically designed for ambiguous ultrasound scenarios, which provides stronger attention guidance to indistinct boundaries, thereby improving the accuracy of structural contour segmentation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed TGMS-UNet is a dual-branch segmentation network that converts geometric contours into sequential representations by calculating the distance and angle variations from boundary points to the centroid. The sequence-image feature alignment module employs global attention and scanning mechanisms to achieve cross-modal fusion and integrate contour prior knowledge, while the feature correction and adaptive fusion module uses a gating mechanism to rectify multi-scale feature misalignment and suppress erroneous information. The network is trained end-to-end, enabling the fusion of multi-scale image features and sequence-guided boundary information within a unified framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results show that TGMS-UNet achieves a Dice of 0.9226 and an IoU of 0.8822 on a private endometrial ultrasound dataset of 1,063 images, and a Dice of 0.8355 and an IoU of 0.8012 on the public BUSI breast ultrasound dataset, outperforming existing mainstream segmentation models on key metrics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed TGMS-UNet demonstrates strong ability to handle ambiguous, low-contrast ultrasound images, particularly in accurately delineating blurred boundaries. By leveraging contour-derived prior information and enhancing feature consistency across scales, it improves segmentation precision and reliability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261467802","type":"journal-article","created":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T05:37:52Z","timestamp":1783748272000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["TGMS-UNet: A dual-branch segmentation network for ultrasound endometrium based on sequence guidance and multi-scale feature correction"],"prefix":"10.1177","volume":"12","author":[{"given":"Qiao","family":"Wei","sequence":"first","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowen","family":"Liang","sequence":"additional","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"},{"name":"Central South University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfen","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Lin","sequence":"additional","affiliation":[{"name":"The Third Affiliated Hospital of Guangzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhili","family":"Guo","sequence":"additional","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kezhen","family":"Wang","sequence":"additional","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Xiao","sequence":"additional","affiliation":[{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Lan","sequence":"additional","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linyuan","family":"Jin","sequence":"additional","affiliation":[{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nian","family":"Hu","sequence":"additional","affiliation":[{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Yu","sequence":"additional","affiliation":[{"name":"Changsha Jiangwan Maternity Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaocheng","family":"Xiao","sequence":"additional","affiliation":[{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5973-4522","authenticated-orcid":false,"given":"Zhiyi","family":"Chen","sequence":"additional","affiliation":[{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"},{"name":"University of South China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiographics.21.6.g01nv211409"},{"key":"e_1_3_7_3_2","volume-title":"Infertility prevalence estimates, 1990\u20132021","author":"Organization WH","year":"2023","unstructured":"Organization WH. 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