{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:35:27Z","timestamp":1780544127444,"version":"3.54.1"},"reference-count":48,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12061052"],"award-info":[{"award-number":["12061052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62161044"],"award-info":[{"award-number":["62161044"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Imaging Syst Tech"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Skin lesion segmentation is a crucial step for skin lesion analysis and subsequent treatment. However, it is still a challenging task due to the irregular and fuzzy lesion borders, and the diversity of skin lesions. In this article, we propose Triple\u2010UNet, an organic combination of three UNet architectures with suitable modules, to automatically segment skin lesions. To enhance the target object region of the image, we design a region of interest enhancement module (ROIE) that uses the predicted score map of the first UNet. The enhanced image and the features learned by the first UNet help the second UNet obtain a better score map. Finally, the results are fine\u2010tuned by the third UNet. We evaluate our algorithm on a publicly available dataset of skin lesion segmentation. Experiments have shown that TripleUNet achieves an accuracy of 92.5% on the ISIC\u20102018 skin lesion segmentation benchmark, with Dice and mIoU of 0.909 and 0.836, respectively, which outperforms the state\u2010of\u2010the\u2010art algorithms.<\/jats:p>","DOI":"10.1002\/ima.23090","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T09:27:14Z","timestamp":1715246834000},"update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A region of interest focused Triple UNet architecture for skin lesion segmentation"],"prefix":"10.1002","volume":"34","author":[{"given":"Guoqing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mathematical Science Inner Mongolia University  Hohhot 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