{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:10:14Z","timestamp":1753870214618,"version":"3.41.2"},"reference-count":38,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3604505"],"award-info":[{"award-number":["2023YFC3604505"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Robotics Computer Surgery"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. Fully automatic segmentation algorithms cannot integrate clinical expertise and correct output errors, while interactive methods can offer a better chance for higher accuracy and reliability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We proposed a new network\u2014RRM\u2010TransUNet for the interactive pancreas segmentation task in CT images aiming to provide more reliable and precise results. The network incorporates Rotary Position Embedding, Root Mean Square Normalisation, and a Mixture of Experts mechanism. An intuitive interface is constructed for user\u2010aided pancreas segmentation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>RRM\u2010TransUNet achieves outstanding performance on multiple datasets, with a Dice Similarity Coefficient (DSC) of 93.82% and an Average Symmetric Surface Distance error (ASSD) of 1.12\u00a0mm on MSD, 93.79%\/1.15\u00a0mm on AMOS, and 93.68%\/1.18\u00a0mm on AbdomenCT\u20101K.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our method outperforms previous methods and provides doctors with an efficient and user\u2010friendly interactive pancreas segmentation experience through the intuitive interface.<\/jats:p><\/jats:sec>","DOI":"10.1002\/rcs.70065","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T20:05:46Z","timestamp":1744315546000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RRM\u2010TransUNet: Deep\u2010Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0272-8467","authenticated-orcid":false,"given":"Yulan","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems Beihang University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems Beihang University  Beijing 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