{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:33:20Z","timestamp":1743154400705,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030881122"},{"type":"electronic","value":"9783030881139"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88113-9_57","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T19:04:24Z","timestamp":1632942264000},"page":"703-714","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Learning Approach for Hepatic Steatosis Estimation from Ultrasound Imaging"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2022-0804","authenticated-orcid":false,"given":"Sara","family":"Colantonio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9779-2731","authenticated-orcid":false,"given":"Antonio","family":"Salvati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1590-7890","authenticated-orcid":false,"given":"Claudia","family":"Caudai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9942-0328","authenticated-orcid":false,"given":"Ferruccio","family":"Bonino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6030-8483","authenticated-orcid":false,"given":"Laura De","family":"Rosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7742-8126","authenticated-orcid":false,"given":"Maria Antonietta","family":"Pascali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7814-5280","authenticated-orcid":false,"given":"Danila","family":"Germanese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8364-9152","authenticated-orcid":false,"given":"Maurizia Rossana","family":"Brunetto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6201-1843","authenticated-orcid":false,"given":"Francesco","family":"Faita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"57_CR1","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.cmpb.2017.12.016","volume":"155","author":"M Biswas","year":"2018","unstructured":"Biswas, M., et al.: Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput. Methods Programs Biomed. 155, 165\u2013177 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"7","key":"57_CR2","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1056\/NEJM200102153440706","volume":"344","author":"AA Bravo","year":"2001","unstructured":"Bravo, A.A., Sheth, S., Chopra, S.: Liver biopsy. N. Engl. J. Med. 344(7), 495\u2013500 (2001)","journal-title":"N. Engl. J. Med."},{"issue":"12","key":"57_CR3","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1007\/s11548-018-1843-2","volume":"13","author":"M Byra","year":"2018","unstructured":"Byra, M.: Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int. J. Comput. Assist. Radiol. Surg. 13(12), 1895\u20131903 (2018). https:\/\/doi.org\/10.1007\/s11548-018-1843-2","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"57_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/jum.15070","volume":"39","author":"W Cao","year":"2019","unstructured":"Cao, W., An, X., Cong, L., Lyu, C., Zhou, Q., Guo, R.: Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease. J. Ultrasound Med. 39(1), 51\u201359 (2019). https:\/\/doi.org\/10.1002\/jum.15070","journal-title":"J. Ultrasound Med."},{"issue":"4","key":"57_CR5","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1002\/jmri.21542","volume":"28","author":"G Cowin","year":"2008","unstructured":"Cowin, G., et al.: Magnetic resonance imaging and spectroscopy for monitoring liver steatosis. J. Magn. Reson. Imaging 28(4), 937\u201345 (2008). https:\/\/doi.org\/10.1002\/jmri.21542","journal-title":"J. Magn. Reson. Imaging"},{"issue":"1","key":"57_CR6","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jhep.2020.03.039","volume":"73","author":"M Eslam","year":"2020","unstructured":"Eslam, M., et al.: A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J. Hepatol. 73(1), 202\u2013209 (2020). https:\/\/doi.org\/10.1016\/j.jhep.2020.03.039","journal-title":"J. Hepatol."},{"issue":"7","key":"57_CR7","doi-asserted-by":"publisher","first-page":"1999.e","DOI":"10.1053\/j.gastro.2019.11.312","volume":"158","author":"M Eslam","year":"2020","unstructured":"Eslam, M., et al.: MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology 158(7), 1999.e-2014.e1 (2020). https:\/\/doi.org\/10.1053\/j.gastro.2019.11.312. Nonalcoholic FattyLiver Disease in 2020","journal-title":"Gastroenterology"},{"issue":"2","key":"57_CR8","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1148\/radiol.2020191160","volume":"295","author":"A Han","year":"2020","unstructured":"Han, A., et al.: Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology 295(2), 342\u2013350 (2020). https:\/\/doi.org\/10.1148\/radiol.2020191160","journal-title":"Radiology"},{"issue":"3","key":"57_CR9","doi-asserted-by":"publisher","first-page":"e91987","DOI":"10.1371\/journal.pone.0091987","volume":"9","author":"T Karlas","year":"2014","unstructured":"Karlas, T., et al.: Non-invasive assessment of hepatic steatosis in patients with NAFLD using controlled attenuation parameter and 1H-MR spectroscopy. PLoS ONE 9(3), e91987 (2014). https:\/\/doi.org\/10.1371\/journal.pone.0091987","journal-title":"PLoS ONE"},{"issue":"8","key":"57_CR10","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1016\/j.ultrasmedbio.2018.03.011","volume":"44","author":"ND Lascio","year":"2018","unstructured":"Lascio, N.D., et al.: Steato-score: non-invasive quantitative assessment of liver fat by ultrasound imaging. Ultrasound Med. Biol. 44(8), 1585\u20131596 (2018). https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2018.03.011","journal-title":"Ultrasound Med. Biol."},{"key":"57_CR11","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1038\/nrgastro.2013.171","volume":"10","author":"R Loomba","year":"2013","unstructured":"Loomba, R., Sanyal, A.: The global NAFLD epidemic. Nat. Rev. Gastroenterol. Hepatol. 10, 686\u2013690 (2013). https:\/\/doi.org\/10.1038\/nrgastro.2013.171","journal-title":"Nat. Rev. Gastroenterol. Hepatol."},{"issue":"5","key":"57_CR12","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1016\/j.jhep.2012.11.021","volume":"58","author":"M Machado","year":"2013","unstructured":"Machado, M., Cortez-Pinto, H.: Non-invasive diagnosis of non-alcoholic fatty liver disease. A critical appraisal. J. Hepatol. 58(5), 1007\u20131019 (2013). https:\/\/doi.org\/10.1016\/j.jhep.2012.11.021","journal-title":"J. Hepatol."},{"issue":"12","key":"57_CR13","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1016\/j.metabol.2009.05.032","volume":"58","author":"M Mancini","year":"2009","unstructured":"Mancini, M., et al.: Sonographic hepatic-renal ratio as indicator of hepatic steatosis: comparison with (1)H magnetic resonance spectroscopy. Metab., Clin. Exp. 58(12), 1724\u20131730 (2009). https:\/\/doi.org\/10.1016\/j.metabol.2009.05.032","journal-title":"Metab., Clin. Exp."},{"issue":"6","key":"57_CR14","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.3390\/diagnostics11061078","volume":"11","author":"SL Popa","year":"2021","unstructured":"Popa, S.L., et al.: Non-alcoholic fatty liver disease: implementing complete automated diagnosis and staging a systematic review. Diagnostics 11(6), 1078 (2021). https:\/\/doi.org\/10.3390\/diagnostics11061078","journal-title":"Diagnostics"},{"key":"57_CR15","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1007\/978-3-642-29216-3_74","volume-title":"Global Trends in Information Systems and Software Applications","author":"S Purushotham","year":"2012","unstructured":"Purushotham, S., Tripathy, B.K.: Evaluation of classifier models using stratified tenfold cross validation techniques. In: Krishna, P.V., Babu, M.R., Ariwa, E. (eds.) ObCom 2011. CCIS, vol. 270, pp. 680\u2013690. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-29216-3_74"},{"key":"57_CR16","doi-asserted-by":"crossref","unstructured":"Reddy, D.S., Bharath, R., Rajalakshmi, P.: A novel computer-aided diagnosis framework using deep learning for classification of fatty liver disease in ultrasound imaging. In: 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1\u20135 (2018)","DOI":"10.1109\/HealthCom.2018.8531118"},{"issue":"4","key":"57_CR17","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1002\/jmri.22775","volume":"34","author":"S Reeder","year":"2011","unstructured":"Reeder, S., Cruite, I., Hamilton, G., Sirlin, C.: Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J. Magn. Reson. Imaging 34(4), 729\u2013749 (2011). https:\/\/doi.org\/10.1002\/jmri.22775","journal-title":"J. Magn. Reson. Imaging"},{"issue":"7","key":"57_CR18","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1016\/S2468-1253(21)00020-0","volume":"6","author":"G Targher","year":"2021","unstructured":"Targher, G., Tilg, H., Byrne, C.D.: Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol. Hepatol. 6(7), 578\u2013588 (2021)","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"57_CR19","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1038\/oby.2011.302","volume":"20","author":"MF Xia","year":"2012","unstructured":"Xia, M.F., et al.: Standardized ultrasound hepatic\/renal ratio and hepatic attenuation rate to quantify liver fat content: an improvement method. Obesity (Silver Spring, Md.) 20, 444\u2013452 (2012)","journal-title":"Obesity (Silver Spring, Md.)"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88113-9_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T19:30:20Z","timestamp":1632943820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88113-9_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030881122","9783030881139"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88113-9_57","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"231","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}