{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:25:10Z","timestamp":1740144310267,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006690","name":"Politecnico di Milano","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006690","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>COVID-19 has spread rapidly worldwide since its initial appearance, creating the need for faster diagnostic methods and tools. Due to the high rate of false-negative RT-PCR tests, the role of chest CT examination has been investigated as an auxiliary procedure. The main goal of this work is to establish a well-defined strategy for 3D segmentation of the airways and lungs of COVID-19 positive patients from CT scans, including detected abnormalities. Their identification and the volumetric quantification could allow an easier classification in terms of gravity, extent and progression of the infection. Moreover, these 3D reconstructions can provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Segmentation process was performed utilizing a proprietary software, starting from six different stacks of chest CT images of subjects with and without COVID-19. In this context, a comparison between manual and automatic segmentation methods of the respiratory system was conducted, to assess the potential value of both techniques, in terms of time consumption, required anatomical knowledge and branch detection, in healthy and pathological conditions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>High-quality 3D models were obtained. They can be utilized to assess the impact of the pathology, by volumetrically quantifying the extension of the affected areas. Indeed, based on the obtained reconstructions, an attempted classification for each patient in terms of the severity of the COVID-19 infection has been outlined.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Automatic algorithms allowed for a substantial reduction in segmentation time. However, a great effort was required for the manual identification of COVID-19 CT manifestations. The developed automated procedure succeeded in obtaining sufficiently accurate models of the airways and the lungs of both healthy patients and subjects with confirmed COVID-19, in a reasonable time.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02466-2","type":"journal-article","created":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T10:08:26Z","timestamp":1628244506000},"page":"1737-1747","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["High-quality chest CT segmentation to assess the impact of COVID-19 disease"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5256-181X","authenticated-orcid":false,"given":"Michele","family":"Bertolini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8976-0369","authenticated-orcid":false,"given":"Alma","family":"Brambilla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0372-0428","authenticated-orcid":false,"given":"Samanta","family":"Dallasta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-8960","authenticated-orcid":false,"given":"Giorgio","family":"Colombo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"2466_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/s41579-020-00459-7","author":"B Hu","year":"2020","unstructured":"Hu B, Guo H, Zhou P, Shi ZL (2020) Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. https:\/\/doi.org\/10.1038\/s41579-020-00459-7","journal-title":"Nat Rev Microbiol"},{"key":"2466_CR2","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/J.IJSU.2020.02.034","volume":"76","author":"C Sohrabi","year":"2020","unstructured":"Sohrabi C, Alsafi Z, O\u2019Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R (2020) World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int J Surg 76:71\u201376. https:\/\/doi.org\/10.1016\/J.IJSU.2020.02.034","journal-title":"Int J Surg"},{"key":"2466_CR3","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1001\/jama.2020.12839","volume":"324","author":"WJ Wiersinga","year":"2020","unstructured":"Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC (2020) Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA J Am Med Assoc 324:782\u2013793. https:\/\/doi.org\/10.1001\/jama.2020.12839","journal-title":"JAMA J Am Med Assoc"},{"key":"2466_CR4","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.1148\/rg.2020200159","volume":"40","author":"TC Kwee","year":"2020","unstructured":"Kwee TC, Kwee RM (2020) Chest CT in COVID-19: What the radiologist needs to know. Radiographics 40:1848\u20131865. https:\/\/doi.org\/10.1148\/rg.2020200159","journal-title":"Radiographics"},{"key":"2466_CR5","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.2020200230","volume":"295","author":"M Chung","year":"2020","unstructured":"Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad ZA, Jacobi A, Li K, Li S, Shan H (2020) CT imaging features of 2019 novel coronavirus (2019-NCoV). Radiology 295:202\u2013207. https:\/\/doi.org\/10.1148\/radiol.2020200230","journal-title":"Radiology"},{"key":"2466_CR6","doi-asserted-by":"publisher","first-page":"4381","DOI":"10.1007\/s00330-020-06801-0","volume":"30","author":"Z Ye","year":"2020","unstructured":"Ye Z, Zhang Y, Wang Y, Huang Z, Song B (2020) Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol 30:4381\u20134389. https:\/\/doi.org\/10.1007\/s00330-020-06801-0","journal-title":"Eur Radiol"},{"key":"2466_CR7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1136\/bcr-2020-236943","volume":"13","author":"ER Schachner","year":"2020","unstructured":"Schachner ER, Spieler B (2020) Three-dimensional (3D) lung segmentation for diagnosis of COVID-19 and the communication of disease impact to the public. BMJ Case Rep 13:19\u201321. https:\/\/doi.org\/10.1136\/bcr-2020-236943","journal-title":"BMJ Case Rep"},{"key":"2466_CR8","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1002\/mrm.21285","volume":"58","author":"YS Tzeng","year":"2007","unstructured":"Tzeng YS, Hoffman E, Cook-Granroth J, Maurer R, Shah N, Mansour J, Tschirren J, Albert M (2007) Comparison of airway diameter measurements from an anthropomorphic airway tree phantom using hyperpolarized 3He MRI and high-resolution computed tomography. Magn Reson Med 58:636\u2013642. https:\/\/doi.org\/10.1002\/mrm.21285","journal-title":"Magn Reson Med"},{"key":"2466_CR9","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1016\/j.compmedimag.2013.07.003","volume":"37","author":"J Rosell","year":"2013","unstructured":"Rosell J, Cabras P (2013) A three-stage method for the 3D reconstruction of the tracheobronchial tree from CT scans. Comput Med Imaging Graph 37:430\u2013437. https:\/\/doi.org\/10.1016\/j.compmedimag.2013.07.003","journal-title":"Comput Med Imaging Graph"},{"key":"2466_CR10","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1016\/S1076-6332(03)80517-2","volume":"9","author":"AP Kiraly","year":"2002","unstructured":"Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM (2002) Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol 9:1153\u20131168. https:\/\/doi.org\/10.1016\/S1076-6332(03)80517-2","journal-title":"Acad Radiol"},{"key":"2466_CR11","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s00408-011-9305-4","volume":"189","author":"C Salito","year":"2011","unstructured":"Salito C, Barazzetti L, Woods JC, Aliverti A (2011) 3D airway tree reconstruction in healthy subjects and emphysema. Lung 189:287\u2013293. https:\/\/doi.org\/10.1007\/s00408-011-9305-4","journal-title":"Lung"},{"key":"2466_CR12","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1109\/42.500140","volume":"15","author":"M Sonka","year":"1996","unstructured":"Sonka M, Park W, Huffman EA (1996) Rule-based detection of intrathoracic airway trees. IEEE Trans Med Imaging 15:314\u2013326. https:\/\/doi.org\/10.1109\/42.500140","journal-title":"IEEE Trans Med Imaging"},{"key":"2466_CR13","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1109\/TMI.2012.2209674","volume":"31","author":"P Lo","year":"2012","unstructured":"Lo P, Van Ginneken B, Reinhardt JM, Yavarna T, De Jong PA, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijanska A, Bauer C, Beichel R, Mendoza CS, Wiemker R, Lee J, Reeves AP, Born S, Weinheimer O, Van Rikxoort EM, Tschirren J, Mori K, Odry B, Naidich DP, Hartmann I, Hoffman EA, Prokop M, Pedersen JH, De Bruijne M (2012) Extraction of airways from CT (EXACT\u201909). IEEE Trans Med Imaging 31:2093\u20132107. https:\/\/doi.org\/10.1109\/TMI.2012.2209674","journal-title":"IEEE Trans Med Imaging"},{"key":"2466_CR14","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1109\/TMI.2003.815905","volume":"22","author":"D Aykac","year":"2003","unstructured":"Aykac D, Huffman EA, McLennan G, Reinhardt JM (2003) Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images. IEEE Trans Med Imaging 22:940\u2013950. https:\/\/doi.org\/10.1109\/TMI.2003.815905","journal-title":"IEEE Trans Med Imaging"},{"key":"2466_CR15","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/42.730394","volume":"17","author":"W Park","year":"1998","unstructured":"Park W, Huffman EA, Sonka M (1998) Segmentation of intrathoracic airway trees: a fuzzy logic approach. IEEE Trans Med Imaging 17:489\u2013497. https:\/\/doi.org\/10.1109\/42.730394","journal-title":"IEEE Trans Med Imaging"},{"key":"2466_CR16","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1109\/TMI.2008.929101","volume":"28","author":"S Ukil","year":"2009","unstructured":"Ukil S, Reinhardt JM (2009) Anatomy-guided lung lobe segmentation in X-ray CT images. IEEE Trans Med Imaging 28:202\u2013214. https:\/\/doi.org\/10.1109\/TMI.2008.929101","journal-title":"IEEE Trans Med Imaging"},{"unstructured":"van Rikxoort E, Baggerman W, Ginneken B (2009) Automatic segmentation of the airway tree from thoracic CT scans using a multi-threshold approach. Second Work Pulm Image Anal: 341\u2013349","key":"2466_CR17"},{"key":"2466_CR18","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1016\/j.chest.2020.06.025","volume":"158","author":"HJA Adams","year":"2020","unstructured":"Adams HJA, Kwee TC, Yakar D, Hope MD, Kwee RM (2020) Chest CT imaging signature of coronavirus disease 2019 infection. In pursuit of the scientific evidence. Chest 158:1885\u20131895. https:\/\/doi.org\/10.1016\/j.chest.2020.06.025","journal-title":"Chest"},{"key":"2466_CR19","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1097\/RTI.0000000000000524","volume":"35","author":"S Simpson","year":"2020","unstructured":"Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, Henry TS, Kanne JP, Kligerman S, Ko JP, Litt H (2020) Radiological society of north America expert consensus statement on reporting chest CT findings related to COVID-19. endorsed by the society of thoracic radiology, the American college of radiology, and RSNA - secondary publication. J Thorac Imaging 35:219\u2013227. https:\/\/doi.org\/10.1097\/RTI.0000000000000524","journal-title":"J Thorac Imaging"},{"key":"2466_CR20","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.media.2016.09.002","volume":"35","author":"PP Rebou\u00e7as Filho","year":"2017","unstructured":"Rebou\u00e7as Filho PP, Cortez PC, da Silva Barros AC, Victor VH, Tavares RSJM (2017) Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal 35:503\u2013516. https:\/\/doi.org\/10.1016\/j.media.2016.09.002","journal-title":"Med Image Anal"},{"key":"2466_CR21","doi-asserted-by":"publisher","first-page":"E204","DOI":"10.1148\/radiol.2021203957","volume":"299","author":"EB Tsai","year":"2021","unstructured":"Tsai EB, Simpson S, Lungren MP, Hershman M, Roshkovan L, Colak E, Erickson BJ, Shih G, Stein A, Kalpathy-Cramer J, Shen J, Hafez M, John S, Rajiah P, Pogatchnik BP, Mongan J, Altinmakas E, Ranschaert ER, Kitamura FC, Topff L, Moy L, Kanne JP, Wu CC (2021) The RSNA international COVID-19 open radiology database (RICORD). Radiology 299:E204\u2013E213. https:\/\/doi.org\/10.1148\/radiol.2021203957","journal-title":"Radiology"},{"key":"2466_CR22","first-page":"365","volume":"11","author":"S Noma","year":"1990","unstructured":"Noma S, Khan A, Herman PG, Rojas KA (1990) High-resolution computed tomography of the pulmonary parenchyma. Semin Ultrasound CT MR 11:365\u2013379","journal-title":"Semin Ultrasound CT MR"},{"key":"2466_CR23","doi-asserted-by":"publisher","first-page":"e200075","DOI":"10.1148\/ryct.2020200075","volume":"2","author":"L Huang","year":"2020","unstructured":"Huang L, Han R, Ai T, Yu P, Kang H, Tao Q, Xia L (2020) Serial quantitative chest CT assessment of COVID-19: a deep learning approach. Radiol Cardiothorac Imaging 2:e200075. https:\/\/doi.org\/10.1148\/ryct.2020200075","journal-title":"Radiol Cardiothorac Imaging"},{"key":"2466_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/s42058-020-00034-2","volume":"3","author":"N Yu","year":"2020","unstructured":"Yu N, Shen C, Yu Y, Dang M, Cai S, Guo Y (2020) Lung involvement in patients with coronavirus disease-19 (COVID-19): a retrospective study based on quantitative CT findings. Chinese J Acad Radiol 3:102\u2013107. https:\/\/doi.org\/10.1007\/s42058-020-00034-2","journal-title":"Chinese J Acad Radiol"},{"key":"2466_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-015-0060-2","volume":"14","author":"P Nardelli","year":"2015","unstructured":"Nardelli P, Khan KA, Corv\u00f2 A, Moore N, Murphy MJ, Twomey M, O\u2019Connor OJ, Kennedy MP, Est\u00e9par RSJ, Maher MM, Cantillon-Murphy P (2015) Optimizing parameters of an open-source airway segmentation algorithm using different CT images. Biomed Eng Online 14:1\u201324. https:\/\/doi.org\/10.1186\/s12938-015-0060-2","journal-title":"Biomed Eng Online"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02466-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02466-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02466-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T03:17:55Z","timestamp":1636600675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02466-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,6]]},"references-count":25,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["2466"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02466-2","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"type":"print","value":"1861-6410"},{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2021,8,6]]},"assertion":[{"value":"29 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This article does not contain any studies with animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human or Animal Rights"}},{"value":"For this type of study, formal consent is not required.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}