{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T18:22:54Z","timestamp":1783189374538,"version":"3.54.6"},"reference-count":48,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel approach that combines the Swin Transformer and Deformable Transformer to enhance overall model performance. We leverage the Swin Transformer\u2019s window attention mechanism to capture local feature information and employ the Deformable Transformer to adjust sampling positions dynamically, accelerating model convergence and aligning it more closely with object shapes and sizes. By amalgamating both Transformer modules and incorporating additional skip connections to minimize information loss, our proposed model excels at rapidly and accurately segmenting CT or X-ray lung images. Experimental results demonstrate the remarkable, showcasing the significant prowess of our model. It surpasses the performance of the standalone Swin Transformer\u2019s Swin Unet and converges more rapidly under identical conditions, yielding accuracy improvements of 0.7% (resulting in 88.18%) and 2.7% (resulting in 98.01%) on the COVID-19 CT scan lesion segmentation dataset and Chest X-ray Masks and Labels dataset, respectively. This advancement has the potential to aid medical practitioners in early diagnosis and treatment decision-making.<\/jats:p>","DOI":"10.7717\/peerj.17005","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T09:22:36Z","timestamp":1709198556000},"page":"e17005","source":"Crossref","is-referenced-by-count":18,"title":["Enhancing medical image segmentation with a multi-transformer U-Net"],"prefix":"10.7717","volume":"12","author":[{"given":"Yongping","family":"Dan","sequence":"first","affiliation":[{"name":"School of Electronic and Information, Zhongyuan University Of Technology, Zhengzhou, Henan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weishou","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Zhongyuan University Of Technology, Zhengzhou, Henan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuebin","family":"Yue","sequence":"additional","affiliation":[{"name":"Research Organization of Science and Technology, Ritsumeikan University, Kusatsu, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhida","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information, Zhongyuan University Of Technology, Zhengzhou, Henan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"10.7717\/peerj.17005\/ref-1","doi-asserted-by":"publisher","first-page":"e607","DOI":"10.7717\/peerj-cs.607","article-title":"Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures","volume":"7","author":"Abedalla","year":"2021","journal-title":"PeerJ Computer Science"},{"issue":"6","key":"10.7717\/peerj.17005\/ref-2","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/34.295913","article-title":"Seeded region growing","volume":"16","author":"Adams","year":"1994","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.7717\/peerj.17005\/ref-3","first-page":"10385","article-title":"Improved road connectivity by joint learning of orientation and segmentation","author":"Batra","year":"2019"},{"issue":"2","key":"10.7717\/peerj.17005\/ref-4","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","article-title":"Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration","volume":"33","author":"Candemir","year":"2013","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.7717\/peerj.17005\/ref-5","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.7717\/peerj.17005\/ref-6","article-title":"Transunet: transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"10.7717\/peerj.17005\/ref-7","first-page":"293","article-title":"Pulmonary vessel segmentation based on orthogonal fused U-Net++ of chest CT images","author":"Cui","year":"2019"},{"key":"10.7717\/peerj.17005\/ref-8","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"10.7717\/peerj.17005\/ref-9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508","article-title":"Medical image segmentation based on u-net: a review","volume":"64","author":"Du","year":"2020","journal-title":"Journal of Imaging Science and Technology"},{"key":"10.7717\/peerj.17005\/ref-10","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.autcon.2018.11.028","article-title":"Autonomous concrete crack detection using deep fully convolutional neural network","volume":"99","author":"Dung","year":"2019","journal-title":"Automation in Construction"},{"key":"10.7717\/peerj.17005\/ref-11","first-page":"3146","article-title":"Dual attention network for scene segmentation","author":"Fu","year":"2019"},{"issue":"8","key":"10.7717\/peerj.17005\/ref-12","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","article-title":"Automatic multi-organ segmentation on abdominal CT with dense V-networks","volume":"37","author":"Gibson","year":"2018","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"10","key":"10.7717\/peerj.17005\/ref-13","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"10.7717\/peerj.17005\/ref-14","first-page":"100","article-title":"Algorithm AS 136: a k-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"Journal of the Royal Statistical Society. 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