{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:21:33Z","timestamp":1760149293494,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673316","16JK1697"],"award-info":[{"award-number":["61673316","16JK1697"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008250","name":"Department of Education Shaanxi Province","doi-asserted-by":"publisher","award":["61673316","16JK1697"],"award-info":[{"award-number":["61673316","16JK1697"]}],"id":[{"id":"10.13039\/501100008250","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Chest X-ray (CXR) images can be used to diagnose a variety of lung diseases, such as tuberculosis, pneumonia, and lung cancer. However, the variation in lung morphology due to differences in age, gender, and the severity of pathology makes high-precision lung segmentation a challenging task. Traditional segmentation networks, such as U-Net, have become the standard architecture and have achieved remarkable results in lung field image segmentation tasks. However, because traditional convolutional operations can only explicitly capture local semantic information, it is difficult to obtain global semantic information, resulting in difficult performance in terms of accuracy requirements in medical practical applications. In recent years, the introduction of Transformer technology to natural language processing has achieved great success in the field of computer vision. In this paper, a new network architecture called TransCotANet is proposed. The network architecture is based on the U-Net architecture with convolutional neural networks (CNNs) as the backbone and extracts global semantic information through symmetric cross-layer connections in the encoder structure, where the encoder stage includes an upsampling module to improve the resolution of the feature map, and uses the dynamic aggregation module CotA to dynamically aggregate multi-scale feature maps and finally obtain more accurate segmentation results. The experimental results show that the method outperformed other methods for lung field image segmentation datasets.<\/jats:p>","DOI":"10.3390\/sym15081480","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T01:27:46Z","timestamp":1690421266000},"page":"1480","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TransCotANet: A Lung Field Image Segmentation Network with Multidimensional Global Feature Dynamic Aggregation"],"prefix":"10.3390","volume":"15","author":[{"given":"Xuebin","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8009-3966","authenticated-orcid":false,"given":"Muyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dehua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5542-9532","authenticated-orcid":false,"given":"Jun","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts & Telecommunications, Xi\u2019an 710121, China"},{"name":"Xi\u2019an Key Laboratory of Big Data and Intelligent Computing, Xi\u2019an 710121, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5007813","DOI":"10.1109\/TIM.2022.3161690","article-title":"LwMLA-NET: A lightweight multi-level attention-based network for segmentation of COVID-19 lungs abnormalities from CT images","volume":"71","author":"Roy","year":"2022","journal-title":"IEEE Trans. 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