{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T16:54:04Z","timestamp":1779900844035,"version":"3.53.1"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030886004","type":"print"},{"value":"9783030886011","type":"electronic"}],"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\/978-3-030-88601-1_16","type":"book-chapter","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T01:06:41Z","timestamp":1634087201000},"page":"159-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Evidential Segmentation of 3D PET\/CT Images"],"prefix":"10.1007","author":[{"given":"Ling","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Decazes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thierry","family":"Den\u0153ux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1007\/s00259-020-05080-7","volume":"48","author":"P Blanc-Durand","year":"2020","unstructured":"Blanc-Durand, P., J\u00e9gou, S., Kanoun, S., et al.: Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET\/CT for total metabolic tumour volume prediction using a convolutional neural network. Eur. J. Nucl. Med. Mol. Imaging 48, 1362\u20131370 (2020)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"2","key":"16_CR2","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/3468.833094","volume":"30","author":"T Den\u0153ux","year":"2000","unstructured":"Den\u0153ux, T.: A neural network classifier based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 30(2), 131\u2013150 (2000)","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"16_CR3","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-06164-7_4","volume-title":"A Guided Tour of Artificial Intelligence Research","author":"T Den\u0153ux","year":"2020","unstructured":"Den\u0153ux, T., Dubois, D., Prade, H.: Representations of uncertainty in AI: beyond probability and possibility. In: Marquis, P., Papini, O., Prade, H. (eds.) A Guided Tour of Artificial Intelligence Research, pp. 119\u2013150. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-06164-7_4"},{"issue":"10","key":"16_CR4","doi-asserted-by":"publisher","first-page":"1715","DOI":"10.1007\/s11548-019-02049-2","volume":"14","author":"H Hu","year":"2019","unstructured":"Hu, H., Decazes, P., Vera, P., Li, H., Ruan, S.: Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy. Int. J. Comput. Assist. Radiol. Surg. 14(10), 1715\u20131724 (2019). https:\/\/doi.org\/10.1007\/s11548-019-02049-2","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"7","key":"16_CR5","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1007\/s00259-018-3953-z","volume":"45","author":"H Ilyas","year":"2018","unstructured":"Ilyas, H., et al.: Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma. Eur. J. Nucl. Med. Mol. Imaging 45(7), 1142\u20131154 (2018)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Isensee, F., Petersen, J., Klein, A., Zimmerer, D., et al.: nnU-Net: Self-adapting framework for U-Net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"8004","DOI":"10.1109\/ACCESS.2019.2963254","volume":"8","author":"H Li","year":"2019","unstructured":"Li, H., Jiang, H., Li, S., et al.: DenseX-Net: an end-to-end model for lymphoma segmentation in whole-body PET\/CT images. IEEE Access 8, 8004\u20138018 (2019)","journal-title":"IEEE Access"},{"key":"16_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/978-3-030-32689-0_3","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures","author":"R Mehta","year":"2019","unstructured":"Mehta, R., Christinck, T., Nair, T., Lemaitre, P., Arnold, D., Arbel, T.: Propagating uncertainty across cascaded medical imaging tasks for improved deep learning inference. In: Greenspan, H., et al. (eds.) CLIP\/UNSURE -2019. LNCS, vol. 11840, pp. 23\u201332. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32689-0_3"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision, pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"16_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/978-3-030-11726-9_28","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"A Myronenko","year":"2019","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311\u2013320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_28"},{"issue":"8","key":"16_CR11","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1016\/j.compmedimag.2014.09.007","volume":"38","author":"D Onoma","year":"2014","unstructured":"Onoma, D., Ruan, S., Thureau, S., et al.: Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm. Comput. Med. Imaging Graph. 38(8), 753\u2013763 (2014)","journal-title":"Comput. Med. Imaging Graph."},{"key":"16_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"16_CR13","doi-asserted-by":"publisher","DOI":"10.1515\/9780691214696","volume-title":"A Mathematical Theory of Evidence","author":"G Shafer","year":"1976","unstructured":"Shafer, G.: A Mathematical Theory of Evidence, vol. 42. Princeton University Press, Princeton (1976)"},{"issue":"9","key":"16_CR14","doi-asserted-by":"publisher","first-page":"6376","DOI":"10.1007\/s10489-021-02327-0","volume":"51","author":"Z Tong","year":"2021","unstructured":"Tong, Z., Xu, P., Den\u0153ux, T.: Evidential fully convolutional network for semantic segmentation. Appl. Intell. 51(9), 6376\u20136399 (2021). https:\/\/doi.org\/10.1007\/s10489-021-02327-0","journal-title":"Appl. Intell."}],"container-title":["Lecture Notes in Computer Science","Belief Functions: Theory and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88601-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T08:36:30Z","timestamp":1710232590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88601-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030886004","9783030886011"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88601-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"13 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BELIEF","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Belief Functions","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"15 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"belief2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.lgi2a.univ-artois.fr\/events\/belief2021\/","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":"37","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":"30","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":"81% - 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)"}}]}}