{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:33:00Z","timestamp":1772771580092,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,13]],"date-time":"2022-02-13T00:00:00Z","timestamp":1644710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 \u00b1 0.2140 and 0.8750 \u00b1 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.<\/jats:p>","DOI":"10.3390\/app12041959","type":"journal-article","created":{"date-parts":[[2022,2,13]],"date-time":"2022-02-13T20:32:30Z","timestamp":1644784350000},"page":"1959","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1563-2978","authenticated-orcid":false,"given":"Joana","family":"Sousa","sequence":"first","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"FEUP\u2014Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1681-2436","authenticated-orcid":false,"given":"Tania","family":"Pereira","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3069-2282","authenticated-orcid":false,"given":"Francisco","family":"Silva","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"FCUP\u2014Faculty of Science, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5392-2808","authenticated-orcid":false,"given":"Miguel C.","family":"Silva","sequence":"additional","affiliation":[{"name":"CHUSJ\u2014Centro Hospitalar e Universit\u00e1rio de S\u00e3o Jo\u00e3o, 4200-319 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-491X","authenticated-orcid":false,"given":"Ana T.","family":"Vilares","sequence":"additional","affiliation":[{"name":"CHUSJ\u2014Centro Hospitalar e Universit\u00e1rio de S\u00e3o Jo\u00e3o, 4200-319 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Cunha","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"UTAD\u2014Institute for Systems and Computer Engineering, University of Tr\u00e1s-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6193-8540","authenticated-orcid":false,"given":"H\u00e9lder P.","family":"Oliveira","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal"},{"name":"FCUP\u2014Faculty of Science, University of Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,13]]},"reference":[{"key":"ref_1","unstructured":"Forum of International Respiratory Societies (2017). 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