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In particular, this work focuses on applications in analysis of acute respiratory distress syndrome \u2013 a critical illness with a mortality rate of 40% that affects 200,000 patients in the United States and 3 million globally each year.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Chest radiographs were obtained from critically ill adults (n = 100), adults diagnosed with acute respiratory distress syndrome (ARDS) (n = 25), and children (n = 100) hospitalized at Michigan Medicine. Physicians annotated the lung field of each radiograph to establish the ground truth. A Total Variation-based Active Contour (TVAC) lung segmentation algorithm was developed and compared to multiple state-of-the-art methods including a deep learning model (U-Net), a random walker algorithm, and an active spline model, using the S\u00f8rensen\u2013Dice coefficient to measure segmentation accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The TVAC algorithm accurately segmented lung fields in all patients in the study. For the adult cohort, an averaged Dice coefficient of 0.86 \u00b10.04 (min: 0.76) was reported for TVAC, 0.89 \u00b10.12 (min: 0.01) for U-Net, 0.74 \u00b10.19 (min: 0.15) for the random walker algorithm, and 0.64 \u00b10.17 (min: 0.20) for the active spline model. For the pediatric cohort, a Dice coefficient of 0.85 \u00b10.04 (min: 0.75) was reported for TVAC, 0.87 \u00b10.09 (min: 0.56) for U-Net, 0.67 \u00b10.18 (min: 0.18) for the random walker algorithm, and 0.61 \u00b10.18 (min: 0.18) for the active spline model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The proposed algorithm demonstrates the most consistent performance of all segmentation methods tested. These results suggest that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12880-020-00514-y","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T20:02:46Z","timestamp":1602792166000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2731-4291","authenticated-orcid":false,"given":"Narathip","family":"Reamaroon","sequence":"first","affiliation":[]},{"given":"Michael W.","family":"Sjoding","sequence":"additional","affiliation":[]},{"given":"Harm","family":"Derksen","sequence":"additional","affiliation":[]},{"given":"Elyas","family":"Sabeti","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Gryak","sequence":"additional","affiliation":[]},{"given":"Ryan P.","family":"Barbaro","sequence":"additional","affiliation":[]},{"given":"Brian D.","family":"Athey","sequence":"additional","affiliation":[]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"issue":"6","key":"514_CR1","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/42.929615","volume":"20","author":"S Hu","year":"2001","unstructured":"Hu S, Hoffman EA, Reinhardt JM. 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KN is a member of the editorial board (Associate Editor) of this journal. The other authors are not supported by, nor maintain any financial interest in, any commercial activity that may be associated with the topic of this article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"116"}}