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Phys. Eng. Express"],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Medical image segmentation is an important technique for analyzing medical imaging data and supporting clinical decision-making. However, existing methods still face challenges in boundary delineation due to non-rigid anatomical deformation caused by respiratory motion and organ displacement, as well as the loss of fine details during hierarchical feature extraction. To address these issues, we propose a multi-scale connectivity-edge pixel-level attention network (MCEPANet). Specifically, we introduce a statistical pixel-level connectivity extraction algorithm to characterize anatomical topology through spatial correlation analysis, thereby improving robustness to organ deformation and inter-subject variability. We further design a connectivity-edge pixel-level attention (CEPA) module to integrate edge features derived from traditional edge detection with learned pixel-level connectivity priors via channel-wise attention. CEPA is embedded at multiple network stages to capture edge information at different scales and improve boundary localization. Experimental results on the Synapse multi-organ abdominal segmentation dataset show that MCEPANet achieves an average DSC score of 84.15% and an average 95% Hausdorff Distance (HD95) of 14.88 mm, outperforming existing state-of-the-art two dimensional segmentation methods. Cross-domain validations on the ACDC cardiac dataset and the ISIC 2018 skin lesion dataset confirm its generalizability, highlighting its versatility for different clinical segmentation tasks.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae7c06","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:48:08Z","timestamp":1781218088000},"page":"045020","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["MCEPANet: a connectivity-edge guided attention network for robust medical image segmentation with multi-scale boundary preservation"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2915-2086","authenticated-orcid":true,"given":"Panpan","family":"Wu","sequence":"first","affiliation":[{"name":"Tianjin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1941-0461","authenticated-orcid":true,"given":"Xiaofeng","family":"Ma","sequence":"additional","affiliation":[{"name":"Tianjin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziping","family":"Zhao","sequence":"additional","affiliation":[{"name":"Tianjin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3850-6527","authenticated-orcid":true,"given":"Dong","family":"He","sequence":"additional","affiliation":[{"name":"Tianjin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YuTing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tianjin Normal University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7179-8097","authenticated-orcid":true,"given":"Rui","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengyong","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Massachusetts Lowell"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"bpexae7c06bib1","first-page":"770","type":"conference-proceedings","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"bpexae7c06bib2","first-page":"2818","type":"conference-proceedings","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"bpexae7c06bib3","first-page":"4700","type":"conference-proceedings","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"bpexae7c06bib4","volume":"vol 30","author":"Vaswani","year":"2017","type":"conference-proceedings"},{"key":"bpexae7c06bib5","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","type":"journal-article","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-01-30","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-11","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-07-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}