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Rebou\u00e7as Filho, An effective approach for CT lung segmentation using mask region-based convolutional neural networks, Artificial Intelligence in Medicine, Volume 103, 2020, 101792, ISSN 0933-3657, doi:10.1016\/j.artmed.2020.101792.","DOI":"10.1016\/j.artmed.2020.101792"},{"key":"10.1016\/j.ejmp.2025.105697_b0170","doi-asserted-by":"crossref","first-page":"73293","DOI":"10.1109\/ACCESS.2020.2987925","article-title":"Hybrid Automatic Lung Segmentation on Chest CT Scans","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"issue":"8","key":"10.1016\/j.ejmp.2025.105697_b0175","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1109\/TMI.2020.2995108","article-title":"Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans","volume":"39","author":"Xie","year":"2020","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.ejmp.2025.105697_b0180","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1002\/mp.13939","article-title":"A fully automatic segmentation algorithm for CT lung images based on random forest","volume":"47","author":"Liu","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.ejmp.2025.105697_b0185","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1007\/s10278-020-00388-0","article-title":"Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement","volume":"33","author":"Liu","year":"2020","journal-title":"J Digit Imaging"},{"key":"10.1016\/j.ejmp.2025.105697_b0190","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s12880-020-00529-5","article-title":"COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet","volume":"21","author":"Saood","year":"2021","journal-title":"BMC Med Imaging"},{"key":"10.1016\/j.ejmp.2025.105697_b0195","doi-asserted-by":"crossref","first-page":"15343","DOI":"10.1007\/s00521-023-08407-1","article-title":"Automated semantic lung segmentation in chest CT images using deep neural network","volume":"35","author":"Murugappan","year":"2023","journal-title":"Neural Comput & Applic"},{"key":"10.1016\/j.ejmp.2025.105697_b0200","doi-asserted-by":"crossref","first-page":"22839","DOI":"10.1007\/s00521-021-06719-8","article-title":"Enhanced lung image segmentation using deep learning","volume":"35","author":"Gite","year":"2023","journal-title":"Neural Comput & Applic"},{"key":"10.1016\/j.ejmp.2025.105697_b0210","unstructured":"Serra, J.P. 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Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction. Multimed Tools Appl. 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