{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:03:06Z","timestamp":1790107386033,"version":"4.0.1"},"reference-count":28,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902282"],"award-info":[{"award-number":["61902282"]}],"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":["62071330"],"award-info":[{"award-number":["62071330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:p>Accurate X-ray computed tomography (CT) image segmentation of the abdominal organs is a key task in automated medical image analysis, with crucial applications in clinical decision-making, computer-aided diagnosis, and surgical planning. However, existing methods still face significant challenges: insufficient capability in modeling long-range contextual dependencies, hindering the adaptability to the complicated morphological variations and spatial relationships of abdominal organs; and inaccurate boundary segmentation due to blurred edges and irregular anatomical structures, particularly in regions with high tissue adhesiveness. To address these issues, we propose an efficient abdominal multi-organ segmentation model, EEA-UNet. Specifically, we design an efficient element-wise adaptive (EEA) attention mechanism integrated into the skip connections to enhance inter-organ feature interactions while maintaining computational efficiency. This module effectively expands the receptive field, improving long-range dependency modeling. An enhanced multi-scale feature fusion (EMF) module is introduced to strengthen decoding capability, coupled with an edge-awareness composite loss function to optimize segmentation accuracy for small organs and boundary regions.<\/jats:p>\n                  <jats:p>Experimental results on the Synapse dataset demonstrate the competitive performance of EEA-UNet, achieving a Dice score of 84.45% and an HD95 of 0.16. Our method demonstrates a favorable trade-off between segmentation accuracy and computational efficiency, showing improved results compared with several existing approaches in both visual comparison and quantitative metrics.<\/jats:p>","DOI":"10.1177\/08953996261462020","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:17:42Z","timestamp":1784290662000},"page":"669-681","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["EEA-UNet: An efficient element-wise adaptive attention-based network for abdominal multi-organ segmentation"],"prefix":"10.1177","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2915-2086","authenticated-orcid":false,"given":"Panpan","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1309-5114","authenticated-orcid":false,"given":"Runpeng","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8719-6389","authenticated-orcid":false,"given":"Ziping","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2332-5689","authenticated-orcid":false,"given":"Jiabei","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-0813","authenticated-orcid":false,"given":"Hengyong","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Ronneberger O Fischer P Brox T. 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