{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:00:57Z","timestamp":1790197257482,"version":"4.1.0"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"03","funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4242016"],"award-info":[{"award-number":["4242016"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Project of Henan Province","award":["262102211049"],"award-info":[{"award-number":["262102211049"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2026,6,18]]},"abstract":"<jats:p>PurposeWe aim to overcome the remaining bottlenecks in COVID-19 lesion segmentation from chest CT\u2014namely, blurred lesion boundaries, false-positive responses from vessels or trachea, and the extreme variability of lesion shape, size, and location\u2014by developing a dual-decoder adversarial residual U2Net algorithm.ApproachDDARes-U2Net, an encoder\u2013dual-decoder architecture, simultaneously performs both lung parenchyma segmentation and lesion segmentation within a unified network. The dual-decoder design incorporates a lung-parenchyma decoder that guides the model to focus specifically on the lung region, eliminating the information loss caused by traditional presegmentation approaches and markedly reducing computational overhead. Furthermore, an edge-loss function is introduced during training to enhance the network\u2019s ability to capture fine-grained lesion boundaries. Comparative experiments are conducted under two settings: within-dataset evaluation and cross-dataset evaluation. Ablation studies are performed to validate the contribution of each architectural component.ResultsThe proposed DDARes-U2Net model, with a relatively modest parameter count of 30.74 M, achieves superior generalization performance and segmentation accuracy compared with state-of-the-art methods. Across both within-center and cross-center experiments on five CT datasets (I to V), DDARes-U2Net consistently ranks first for Dice and IoU. The largest gain is observed when the model trained on Dataset IV is tested on Dataset II, delivering +3.41 pp Dice versus ARes-U2Net++ and +5.51 pp versus ARes-U2Net, confirming its superior generalizability and clinical applicability.ConclusionsThe unified dual-decoder framework with explicit edge-aware supervision provides accurate, robust, and generalizable COVID-19 lesion segmentation, achieving the highest Dice and IoU in both intradataset and cross-center tests on five independent CT cohorts.<\/jats:p>","DOI":"10.1117\/1.jmi.13.3.034002","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:00:48Z","timestamp":1781816448000},"page":"1-21","source":"Crossref","is-referenced-by-count":0,"title":["DDARes\u2013U2Net: a dual-decoder adversarial residual U2Net algorithm for segmentation of COVID-19 pneumonia lesions"],"prefix":"10.1117","volume":"13","author":[{"given":"Xiao","family":"Li","sequence":"first","affiliation":[{"name":"Zhongyuan University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fujiao","family":"Ju","sequence":"additional","affiliation":[{"name":"Beijing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2407-8518","authenticated-orcid":false,"given":"Haoran","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhengzhou University of Light Industry"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-13\/issue-03\/034002\/DDAResU2Net--a-dual-decoder-adversarial-residual-U2Net-algorithm-for\/10.1117\/1.JMI.13.3.034002.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:04:48Z","timestamp":1790193888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-13\/issue-03\/034002\/DDAResU2Net--a-dual-decoder-adversarial-residual-U2Net-algorithm-for\/10.1117\/1.JMI.13.3.034002.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,18]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2026,5,1]]}},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.13.3.034002","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,18]]}}}