{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T01:53:51Z","timestamp":1775786031663,"version":"3.50.1"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100009093","name":"Scuola Normale Superiore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100009093","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":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We developed an automated analysis pipeline, the <jats:italic>LungQuant<\/jats:italic> system, based on a cascade of two U-nets. The first one (U-net<jats:inline-formula><jats:alternatives><jats:tex-math>$$_1$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msub>\n                      <mml:mrow\/>\n                      <mml:mn>1<\/mml:mn>\n                    <\/mml:msub>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) is devoted to the identification of the lung parenchyma; the second one (U-net<jats:inline-formula><jats:alternatives><jats:tex-math>$$_2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:msub>\n                      <mml:mrow\/>\n                      <mml:mn>2<\/mml:mn>\n                    <\/mml:msub>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice Similarity Coefficients. The accuracy in predicting the CT-Severity Score (CT-SS) of the <jats:italic>LungQuant<\/jats:italic> system has been also evaluated.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Both the volumetric DSC (vDSC) and the accuracy showed a dependency on the annotation quality of the released data samples. On an independent dataset (COVID-19-CT-Seg), both the vDSC and the surface DSC (sDSC) were measured between the masks predicted by <jats:italic>LungQuant<\/jats:italic> system and the reference ones. The vDSC (sDSC) values of 0.95\u00b10.01 and 0.66\u00b10.13 (0.95\u00b10.02 and 0.76\u00b10.18, with 5 mm tolerance) were obtained for the segmentation of lungs and COVID-19 lesions, respectively. The system achieved an accuracy of 90% in CT-SS identification on this benchmark dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of vDSC measures, the U-net segmentation strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent test sets, demonstrating the satisfactory generalization ability of the <jats:italic>LungQuant<\/jats:italic>.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02501-2","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T14:02:37Z","timestamp":1635256957000},"page":"229-237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0900-0421","authenticated-orcid":false,"given":"Francesca","family":"Lizzi","sequence":"first","affiliation":[]},{"given":"Abramo","family":"Agosti","sequence":"additional","affiliation":[]},{"given":"Francesca","family":"Brero","sequence":"additional","affiliation":[]},{"given":"Raffaella Fiamma","family":"Cabini","sequence":"additional","affiliation":[]},{"given":"Maria Evelina","family":"Fantacci","sequence":"additional","affiliation":[]},{"given":"Silvia","family":"Figini","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Lascialfari","sequence":"additional","affiliation":[]},{"given":"Francesco","family":"Laruina","sequence":"additional","affiliation":[]},{"given":"Piernicola","family":"Oliva","sequence":"additional","affiliation":[]},{"given":"Stefano","family":"Piffer","sequence":"additional","affiliation":[]},{"given":"Ian","family":"Postuma","sequence":"additional","affiliation":[]},{"given":"Lisa","family":"Rinaldi","sequence":"additional","affiliation":[]},{"given":"Cinzia","family":"Talamonti","sequence":"additional","affiliation":[]},{"given":"Alessandra","family":"Retico","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"2501_CR1","doi-asserted-by":"publisher","unstructured":"An P, Xu S, Harmon SA, Turkbey EB, Sanford TH, Amalou A, Kassin M, Varble N, Blain M, Anderson V, Patella F, Carrafiello G, Turkbey BT, Wood BJ (2020) CT Images in COVID-19. https:\/\/doi.org\/10.7937\/tcia.2020.gqry-nc81","DOI":"10.7937\/tcia.2020.gqry-nc81"},{"issue":"7","key":"2501_CR2","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1007\/s11547-020-01237-4","volume":"125","author":"M Carotti","year":"2020","unstructured":"Carotti M, Salaffi F, Sarzi-Puttini P, Agostini A, Borgheresi A, Minorati D, Galli M, Marotto D, Giovagnoni A (2020) Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists. Radiologia Medica 125(7):636\u2013646. https:\/\/doi.org\/10.1007\/s11547-020-01237-4","journal-title":"Radiologia Medica"},{"key":"2501_CR3","unstructured":"Chollet F (2015) Keras. https:\/\/keras.io"},{"key":"2501_CR4","doi-asserted-by":"publisher","unstructured":"Fang X, Kruger U, Homayounieh F, Chao H, Zhang J, Digumarthy SR, Arru CD, Kalra MK, Yan P (2021) Association of AI quantified COVID-19 chest CT and patient outcome. International Journal of Computer Assisted Radiology and Surgery. https:\/\/doi.org\/10.1007\/s11548-020-02299-5. URL http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/33484428","DOI":"10.1007\/s11548-020-02299-5"},{"issue":"11","key":"2501_CR5","doi-asserted-by":"publisher","first-page":"3619","DOI":"10.1109\/TMI.2020.3001036","volume":"39","author":"X Fang","year":"2020","unstructured":"Fang X, Yan P (2020) Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Transactions on Medical Imaging 39(11):3619\u20133629. https:\/\/doi.org\/10.1109\/TMI.2020.3001036","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2501_CR6","doi-asserted-by":"publisher","unstructured":"Fortin JP, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnis M, Phillips ML, Trivedi MH, Weissman MM, Shinohara RT (2018) Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167:104\u2013120. https:\/\/doi.org\/10.1016\/j.neuroimage.2017.11.024. URL http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29155184http:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S105381191730931X","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"2501_CR7","doi-asserted-by":"crossref","unstructured":"Hofmanninger J, Prayer F, Pan J, R\u00f6hrich S, Prosch H, Langs G (2020) Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem. arXiv 2","DOI":"10.1186\/s41747-020-00173-2"},{"issue":"11","key":"2501_CR8","doi-asserted-by":"publisher","first-page":"5941","DOI":"10.1002\/mp.14424","volume":"47","author":"KJ Kiser","year":"2020","unstructured":"Kiser KJ, Ahmed S, Stieb S, Mohamed AS, Elhalawani H, Park PY, Doyle NS, Wang BJ, Barman A, Li Z, Zheng WJ, Fuller CD, Giancardo L (2020) PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines. Medical Physics 47(11):5941\u20135952. https:\/\/doi.org\/10.1002\/mp.14424","journal-title":"Medical Physics"},{"issue":"3","key":"2501_CR9","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s10278-021-00460-3","volume":"34","author":"KJ Kiser","year":"2021","unstructured":"Kiser KJ, Barman A, Stieb S, Fuller CD, Giancardo L (2021) Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow. Journal of Digital Imaging 34(3):541\u2013553. https:\/\/doi.org\/10.1007\/s10278-021-00460-3","journal-title":"Journal of Digital Imaging"},{"issue":"1","key":"2501_CR10","doi-asserted-by":"publisher","first-page":"E18","DOI":"10.1148\/RADIOL.2020202439","volume":"298","author":"N Lessmann","year":"2021","unstructured":"Lessmann N, S\u00e1nchez CI, Beenen L, Boulogne LH, Brink M, Calli E, Charbonnier JP, Dofferhoff T, van Everdingen WM, Gerke PK, Geurts B, Gietema HA, Groeneveld M, van Harten L, Hendrix N, Hendrix W, Huisman HJ, I\u0161gum I, Jacobs C, Kluge R, Kok M, Krdzalic J, Lassen-Schmidt B, van Leeuwen K, Meakin J, Overkamp M, van Rees Vellinga T, van Rikxoort EM, Samperna R, Schaefer-Prokop C, Schalekamp S, Scholten ET, Sital C, St\u00f6ger JL, Teuwen J, Venkadesh KV, de Vente C, Vermaat M, Xie W, de Wilde B, Prokop M, van Ginneken B (2021) Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence. Radiology 298(1):E18\u2013E28. https:\/\/doi.org\/10.1148\/RADIOL.2020202439","journal-title":"Radiology"},{"key":"2501_CR11","doi-asserted-by":"publisher","unstructured":"Ma J, Wang Y, An X, Ge C, Yu Z, Chen J, Zhu Q, Dong G, He J, He Z, Cao T, Zhu Y, Nie Z, Yang X (2020) Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation. Medical Physics. https:\/\/doi.org\/10.1002\/mp.14676","DOI":"10.1002\/mp.14676"},{"key":"2501_CR12","doi-asserted-by":"publisher","unstructured":"Morozov SP, Andreychenko AE, Pavlov NA, Vladzymyrskyy AV, Ledikhova NV, Gombolevskiy VA, Blokhin IA, Gelezhe PB, Gonchar AV, Chernina V (2020) MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset. medRxiv p. 2020.05.20.20100362. https:\/\/doi.org\/10.1101\/2020.05.20.20100362. URL http:\/\/medrxiv.org\/content\/early\/2020\/05\/22\/2020.05.20.20100362.abstract","DOI":"10.1101\/2020.05.20.20100362"},{"key":"2501_CR13","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9351:234\u2013241. https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2501_CR14","doi-asserted-by":"publisher","unstructured":"Xie W, Jacobs C, Charbonnier JP, van Ginneken B (2020) Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans. IEEE Transactions on Medical Imaging 1\u20131. https:\/\/doi.org\/10.1109\/tmi.2020.2995108","DOI":"10.1109\/tmi.2020.2995108"},{"key":"2501_CR15","doi-asserted-by":"publisher","unstructured":"Yang J, Sharp G, Veeraraghavan H, van Elmpt W, Dekker A, Lustberg T, Gooding M (2017) Data from Lung CT Segmentation Challenge. The Cancer Imaging Archive. https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.3r3fvz08","DOI":"10.7937\/K9\/TCIA.2017.3r3fvz08"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02501-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02501-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02501-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T03:04:16Z","timestamp":1642993456000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02501-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":15,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["2501"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02501-2","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"26 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 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":"Conflict 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. Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and informed consent"}}]}}