{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:52:37Z","timestamp":1778773957050,"version":"3.51.4"},"reference-count":39,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Deep learning technology has made remarkable progress in pancreatic image segmentation tasks. However, annotating 3D medical images is time\u2010consuming and requires expertise, and existing semi\u2010supervised segmentation methods perform poorly in the segmentation task of organs with blurred edges in enhanced CT such as the pancreas.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To address the challenges of limited labeled data and indistinct boundaries of regions of interest (ROI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose Edge\u2010Biased Consistency Regularization (EBC\u2010Net). 3D edge detection is employed to construct edge perturbations and integrate edge prior information into limited data, aiding the network in learning from unlabeled data. Additionally, due to the one\u2010sidedness of a single perturbation space, we expand the dual\u2010level perturbation space of both images and features to more efficiently focus the model's attention on the edges of the ROI. Finally, inspired by the clinical habits of doctors, we propose a 3D Anatomical Invariance Extraction Module and Anatomical Attention to capture anatomy\u2010invariant features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Extensive experiments have demonstrated that our method outperforms state\u2010of\u2010the\u2010art methods in semi\u2010supervised pancreas image segmentation. Moreover, it can better preserve the morphology of pancreatic organs and excel at edges region accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Incorporated with edge prior knowledge, our method mixes disturbances in dual\u2010perturbation space, which shifts the network's attention to the fuzzy edge region using a few labeled samples. These ideas have been verified on the pancreas segmentation dataset.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17323","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T20:42:21Z","timestamp":1721767341000},"page":"8260-8271","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["EBC\u2010Net: 3D semi\u2010supervised segmentation of pancreas based on edge\u2010biased consistency regularization in dual perturbation space"],"prefix":"10.1002","volume":"51","author":[{"given":"Zheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shipeng","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101884"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation.International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.Springer 2015;234\u2010241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-34048-2_56"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.10.060"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12419"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106051"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"LiS ZhangC HeX.Shape\u2010aware semi\u2010supervised 3D semantic segmentation for medical images. 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