{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:35:20Z","timestamp":1783010120647,"version":"3.54.5"},"reference-count":31,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,2,9]],"date-time":"2025-02-09T00:00:00Z","timestamp":1739059200000},"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":["11801511"],"award-info":[{"award-number":["11801511"]}],"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":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Abdominal multi\u2010organ segmentation remains a challenging task. Semi\u2010supervised domain adaptation (SSDA) has emerged as an innovative solution. However, SSDA frameworks based on UNet struggle to capture multi\u2010scale and global information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Our work aimed to propose a novel SSDA method to achieve more accurate abdominal multi\u2010organ segmentation with limited labeled target domain data, which has a superior ability to capture the multi\u2010scale features and integrate local and global information effectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The proposed network is based on UNet. In the encoder part, a scale\u2010aware with domain\u2010specific batch normalization (SAD) module is integrated to adaptively extract multi\u2010scale features and to get better generalization across source and target domains. In the bottleneck part, a global\u2010local fusion (GLF) module is utilized for capturing and integrating both local and global information. They are integrated into the framework of self\u2010ensembling mean\u2010teacher (SE\u2010MT) to enhance the model's capability to learn common features across source and target domains.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To validate the performance of the proposed model, we evaluated it on the public CHAOS and BTCV datasets. For CHAOS, the proposed method obtains an average DSC of 88.97% and ASD of 1.12\u00a0mm with only 20% labeled target data. For BTCV, it achieves an average DSC of 88.95% and ASD of 1.13\u00a0mm with 20% labeled target data. Compared with the state\u2010of\u2010the\u2010art methods, DSC and ASD increased by at least 0.72% and 0.33\u00a0mm on CHAOS, 1.29% and 0.06\u00a0mm on BTCV, respectively. Ablation studies were also conducted to verify the contribution of each component of the model. The proposed method achieves a DSC improvement of 3.17% over the baseline with 20% labeled target data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed SSDA method for abdominal multi\u2010organ segmentation has a powerful ability to extract multi\u2010scale and more global features, significantly improving segmentation accuracy and robustness.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.70008","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T07:12:14Z","timestamp":1739257934000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A semi\u2010supervised domain adaptation method with scale\u2010aware and global\u2010local fusion for abdominal multi\u2010organ segmentation"],"prefix":"10.1002","volume":"26","author":[{"given":"Kexin","family":"Han","sequence":"first","affiliation":[{"name":"School of Science Zhejiang University of Science and 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