{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T13:53:37Z","timestamp":1761746017036,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031606649"},{"type":"electronic","value":"9783031606656"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-60665-6_7","type":"book-chapter","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T19:21:12Z","timestamp":1719516072000},"page":"95-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Cascade Approach for\u00a0Automatic Segmentation of\u00a0Coronary Arteries Calcification in\u00a0Computed Tomography Images Using Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9493-0273","authenticated-orcid":false,"given":"Alan","family":"de C. Ara\u00fajo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0423-2514","authenticated-orcid":false,"given":"Arist\u00f3fanes C.","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o M.","family":"Pedrosa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2041-7538","authenticated-orcid":false,"given":"Italo F. S.","family":"Silva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3303-3346","authenticated-orcid":false,"given":"Jo\u00e3o O. B.","family":"Diniz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"issue":"9","key":"7_CR1","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/TMI.2019.2899534","volume":"38","author":"BD de Vos","year":"2019","unstructured":"de Vos, B.D., Wolterink, J.M., Leiner, T., de Jong, P.A., Lessmann, N., I\u0161gum, I.: Direct automatic coronary calcium scoring in cardiac and chest CT. IEEE Trans. Med. Imaging 38(9), 2127\u20132138 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"7_CR2","doi-asserted-by":"publisher","first-page":"7262","DOI":"10.1002\/mp.15870","volume":"49","author":"B Follmer","year":"2022","unstructured":"Follmer, B., et al.: Active multitask learning with uncertainty-weighted loss for coronary calcium scoring. Med. Phys. 49(11), 7262\u20137277 (2022)","journal-title":"Med. Phys."},{"issue":"11","key":"7_CR3","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.diii.2021.05.004","volume":"102","author":"N Gogin","year":"2021","unstructured":"Gogin, N., et al.: Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning. Diagn. Intervent. Imaging 102(11), 683\u2013690 (2021)","journal-title":"Diagn. Intervent. Imaging"},{"issue":"10053","key":"7_CR4","first-page":"2127","volume":"388","author":"H Wang","year":"2019","unstructured":"Wang, H., et al.: systematic analysis for the global burden of disease study 2015. Lancet 388(10053), 2127\u20132138 (2019)","journal-title":"Lancet"},{"issue":"3","key":"7_CR5","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.crad.2019.10.012","volume":"75","author":"W Wang","year":"2020","unstructured":"Wang, W., et al.: Coronary artery calcium score quantification using a deep-learning algorithm. Clin. Radiol. 75(3), 237-e11 (2020)","journal-title":"Clin. Radiol."},{"issue":"3","key":"7_CR6","first-page":"100127","volume":"18","author":"A Yoshida","year":"2021","unstructured":"Yoshida, A., Lee, Y., Yoshimura, N., Kuramoto, T., Hasegawa, A., Kanazawa, T.: Automated heart segmentation using U-net in pediatric cardiac CT. Sensors 18(3), 100127 (2021)","journal-title":"Sensors"},{"issue":"3","key":"7_CR7","first-page":"667","volume":"101","author":"W Zhang","year":"2020","unstructured":"Zhang, W., Zhang, J., Du, X., Zhang, Y., Li, S.: An end-to-end joint learning framework of artery-specific coronary calcium scoring in non-contrast cardiac CT. Computing 101(3), 667\u2013678 (2020)","journal-title":"Computing"},{"key":"7_CR8","unstructured":"Chinchor, N., Sundheim, B.M.: MUC-5 evaluation metrics. In: Fifth Message Understanding Conference (MUC-5): Proceedings of a Conference Held in Baltimore, Maryland, pp. 25\u201327 (2020)"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482\u20137491 (2018)","DOI":"10.1109\/CVPR.2018.00781"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"7_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"7_CR12","volume-title":"Digital Image Processing","author":"RC Gonzalez","year":"2009","unstructured":"Gonzalez, R.C.: Digital Image Processing, 2nd edn. Pearson Education, India (2009)","edition":"2"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: A three-stage self supervised deep learning network for automatic calcium scoring of cardiac computed tomography images. In: 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE (2022)","DOI":"10.1109\/DICTA56598.2022.10034572"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Wireless Mobile Communication and Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60665-6_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T22:45:15Z","timestamp":1732315515000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60665-6_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606649","9783031606656"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60665-6_7","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MobiHealth","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Mobile Communication and Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vila Real","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mobihealth2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mobihealth.eai-conferences.org\/2023\/","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":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","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":"33","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":"30% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}