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Express"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In recent years, spinal x-ray image segmentation has played a vital role in the computer-aided diagnosis of various adolescent spinal disorders. However, due to the complex morphology of lesions and the fact that most existing methods are tailored to single-disease scenarios, current segmentation networks struggle to balance local detail preservation and global structural understanding across different disease types. As a result, they often suffer from limited accuracy, insufficient robustness, and poor adaptability. To address these challenges, we propose a novel fully automated spinal segmentation network, DCE-UNet, which integrates the local modeling strength of convolutional neural networks (CNNs) with the global contextual awareness of Transformers. The network introduces several architectural and feature fusion innovations. Specifically, a lightweight Transformer module is incorporated in the encoder to model high-level semantic features and enhance global contextual understanding. In the decoder, a Rec-Block module combining residual convolution and channel attention is designed to improve feature reconstruction and multi-scale fusion during the upsampling process. Additionally, the downsampling feature extraction path integrates a novel DC-Block that fuses channel and spatial attention mechanisms, enhancing the network\u2019s ability to represent complex lesion structures. Experiments conducted on a self-constructed large-scale multi-disease adolescent spinal x-ray dataset demonstrate that DCE-UNet achieves a Dice score of 91.3%, a mean Intersection over Union (mIoU) of 84.1, and a Hausdorff Distance (HD) of 4.007, outperforming several state-of-the-art comparison networks. Validation on real segmentation tasks further confirms that DCE-UNet delivers consistently superior performance across various lesion regions, highlighting its strong adaptability to multiple pathologies and promising potential for clinical application.<\/jats:p>","DOI":"10.1088\/2057-1976\/adfde9","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T22:48:07Z","timestamp":1755816487000},"page":"055022","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DCE-UNet: a transformer-based fully automated segmentation network for multiple adolescent spinal disorders in x-ray images"],"prefix":"10.1088","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0284-5463","authenticated-orcid":true,"given":"Zhilong","family":"Xue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7311-1401","authenticated-orcid":true,"given":"Shuangcheng","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqun","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7810-4082","authenticated-orcid":true,"given":"Zhiwu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"bpexadfde9bib1","doi-asserted-by":"publisher","first-page":"S247","DOI":"10.1016\/j.otsr.2015.06.012","article-title":"Adolescent spinal pain: the pediatric orthopedist\u2019s point of view","volume":"101","author":"French Society of Spine Surgery (SFCR","year":"2015","journal-title":"Orthopaedics & Traumatology: Surgery & Research"},{"key":"bpexadfde9bib2","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1016\/S0140-6736(08)60658-3","article-title":"Adolescent idiopathic scoliosis","volume":"371","author":"Weinstein","year":"2008","journal-title":"Lancet"},{"key":"bpexadfde9bib3","first-page":"337","article-title":"Trauma to the thoracic and lumbar spine in the adolescent","volume":"44","author":"Clark","year":"2001","journal-title":"Canadian Journal of Surgery"},{"key":"bpexadfde9bib4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1001\/jama.2017.19342","article-title":"Screening for adolescent idiopathic scoliosis: US preventive services task force recommendation statement","volume":"319","author":"US Preventive Services Task Force","year":"2018","journal-title":"JAMA"},{"key":"bpexadfde9bib5","first-page":"24","article-title":"X-raying of the lumbar spine","volume":"2","author":"Ayodeji","year":"2016","journal-title":"International Journal of Neurologic Physical Therapy"},{"key":"bpexadfde9bib6","doi-asserted-by":"publisher","first-page":"3341","DOI":"10.1038\/s41598-023-49923-3","article-title":"VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal x-rays","volume":"14","author":"Chen","year":"2024","journal-title":"Sci. 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