{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T07:16:28Z","timestamp":1784618188243,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Medical images are used as an important basis for diagnosing diseases, among which CT images are seen as an important tool for diagnosing lung lesions. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, a deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. However, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with multi-attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net method, an edge feature fusion module uses the Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, the Tversky loss function is adopted for the segmentation network for small lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over union (IOU) of the proposed SMA-Net model are 86.1% and 77.8%, respectively, which are better than those in most existing segmentation networks.<\/jats:p>","DOI":"10.3390\/s23052546","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T02:10:46Z","timestamp":1677463846000},"page":"2546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Multi-Attention Segmentation Networks Combined with the Sobel Operator for Medical Images"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8228-5422","authenticated-orcid":false,"given":"Fangfang","family":"Lu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201399, China"},{"name":"Department of Electronic Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9673-5906","authenticated-orcid":false,"given":"Chi","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201399, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201399, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-3999","authenticated-orcid":false,"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 201399, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leida","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1148\/radiol.2020200642","article-title":"Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases","volume":"296","author":"Ai","year":"2020","journal-title":"Radiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E115","DOI":"10.1148\/radiol.2020200432","article-title":"Sensitivity of chest CT for COVID-19: Comparison to RT-PCR","volume":"296","author":"Fang","year":"2020","journal-title":"Radiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e047110","DOI":"10.1136\/bmjopen-2020-047110","article-title":"False-negative RT-PCR for COVID-19 and a diagnostic risk score: A retrospective cohort study among patients admitted to hospital","volume":"11","author":"Macleod","year":"2021","journal-title":"BMJ Open"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109947","DOI":"10.1016\/j.chaos.2020.109947","article-title":"Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review","volume":"138","author":"Swapnarekha","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.7189\/jogh.10.010347","article-title":"Combination of CT and RT-PCR in the screening or diagnosis of COVID-19","volume":"10","author":"Wang","year":"2020","journal-title":"J. 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