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However, accurate lung region segmentation is still challenging due to the following three issues: (1) inaccurate lung region segmentation boundaries, (2) existence of lesion\u2010related artifacts (e.g., opacity and pneumonia), and (3) lack of the ability to utilize multiscale information. To address these issues, we propose an edge\u2010assisted computing and mask attention based network (called EAM\u2010Net), which consists of an encoder\u2010decoder network, an edge\u2010assisted computing module, and multiple mask attention modules. Based on the encoder\u2010decoder structure, an edge\u2010assisted computing module is first proposed, which integrates the feature maps of the shallow encoding layers for edge prediction, and uses the edge evidence map as a strong cue to guide the lung region segmentation, thereby refining the lung region segmentation boundaries. We further design a mask attention module after each decoding layer, which employs a mask attention operation to make the model focus on lung regions while suppressing the lesion\u2010related artifacts. Besides, a multiscale aggregation loss is proposed to optimize EAM\u2010Net. Extensive experiments on the JSRT, Shenzhen, and Montgomery datasets demonstrate that EAM\u2010Net outperforms existing state\u2010of\u2010the\u2010art lung region segmentation methods.<\/jats:p>","DOI":"10.1155\/2023\/8589867","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T01:35:05Z","timestamp":1685583305000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Edge\u2010Assisted Computing and Mask Attention Based Network for Lung Region Segmentation"],"prefix":"10.1155","volume":"2023","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Like","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7815-2245","authenticated-orcid":false,"given":"Xiaoyu","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e318216ee6b"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(18)30127-X"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-01917-1"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.04.009"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"e_1_2_11_6_2","doi-asserted-by":"crossref","unstructured":"LongJ. 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