{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T02:35:51Z","timestamp":1790130951696,"version":"4.0.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030671938","type":"print"},{"value":"9783030671945","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-67194-5_10","type":"book-chapter","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T06:29:19Z","timestamp":1610519359000},"page":"85-98","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET\/CT Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1020-4966","authenticated-orcid":false,"given":"Mohamed A.","family":"Naser","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9515-5616","authenticated-orcid":false,"given":"Lisanne V.","family":"van Dijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9166-6286","authenticated-orcid":false,"given":"Renjie","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0503-0175","authenticated-orcid":false,"given":"Kareem A.","family":"Wahid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-3994","authenticated-orcid":false,"given":"Clifton D.","family":"Fuller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21590","volume":"70","author":"RL Siegel","year":"2020","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2020. CA Cancer J. Clin. 70, 7\u201330 (2020). https:\/\/doi.org\/10.3322\/caac.21590","journal-title":"CA Cancer J. Clin."},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1016\/j.ijrobp.2008.01.012","volume":"72","author":"DI Rosenthal","year":"2008","unstructured":"Rosenthal, D.I., et al.: Beam path toxicities to non-target structures during intensity-modulated radiation therapy for head and neck cancer. Int. J. Radiat. Oncol. Biol. Phys. 72, 747\u2013755 (2008). https:\/\/doi.org\/10.1016\/j.ijrobp.2008.01.012","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10_CR3","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s00066-014-0634-0","volume":"190","author":"H Vorwerk","year":"2014","unstructured":"Vorwerk, H., et al.: Protection of quality and innovation in radiation oncology: the prospective multicenter trial the German Society of Radiation Oncology (DEGRO-QUIRO study). Strahlentherapie und Onkol. 190, 433\u2013443 (2014)","journal-title":"Strahlentherapie und Onkol."},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1016\/j.ijrobp.2006.01.014","volume":"65","author":"AC Riegel","year":"2006","unstructured":"Riegel, A.C., et al.: Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET\/CT fusion. Int. J. Radiat. Oncol. Biol. Phys. 65, 726\u2013732 (2006). https:\/\/doi.org\/10.1016\/j.ijrobp.2006.01.014","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10_CR5","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.semradonc.2005.01.005","volume":"15","author":"C Rasch","year":"2005","unstructured":"Rasch, C., Steenbakkers, R., Van Herk, M.: Target definition in prostate, head, and neck. Semin. Radiat. Oncol. 15, 136\u2013145 (2005). https:\/\/doi.org\/10.1016\/j.semradonc.2005.01.005","journal-title":"Semin. Radiat. Oncol."},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1016\/j.ijrobp.2006.12.039","volume":"68","author":"SL Breen","year":"2007","unstructured":"Breen, S.L., et al.: Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. Int. J. Radiat. Oncol. Biol. Phys. 68, 763\u2013770 (2007). https:\/\/doi.org\/10.1016\/j.ijrobp.2006.12.039","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10_CR7","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1515\/raon-2016-0023","volume":"50","author":"B Segedin","year":"2016","unstructured":"Segedin, B., Petric, P.: Uncertainties in target volume delineation in radiotherapy\u2013are they relevant and what can we do about them? Radiol. Oncol. 50, 254\u2013262 (2016)","journal-title":"Radiol. Oncol."},{"key":"10_CR8","first-page":"6","volume":"1","author":"CM Anderson","year":"2014","unstructured":"Anderson, C.M., et al.: Interobserver and intermodality variability in GTV delineation on simulation CT, FDG-PET, and MR images of head and neck cancer. Jacobs J. Radiat. Oncol. 1, 6 (2014)","journal-title":"Jacobs J. Radiat. Oncol."},{"issue":"2","key":"10_CR9","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","volume":"7","author":"Y Guo","year":"2017","unstructured":"Guo, Y., Liu, Yu., Georgiou, T., Lew, M.S.: A review of semantic segmentation using deep neural networks. Int. J. Multimed. Inf. Retr. 7(2), 87\u201393 (2017). https:\/\/doi.org\/10.1007\/s13735-017-0141-z","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Garcia-Rodriguez, J.: A Review on Deep Learning Techniques Applied to Semantic Segmentation (2017)","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"977","DOI":"10.3389\/fonc.2019.00977","volume":"9","author":"L Boldrini","year":"2019","unstructured":"Boldrini, L., Bibault, J.-E., Masciocchi, C., Shen, Y., Bittner, M.-I.: Deep learning: a review for the radiation oncologist. Front. Oncol. 9, 977 (2019)","journal-title":"Front. Oncol."},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.semradonc.2019.02.001","volume":"29","author":"CE Cardenas","year":"2019","unstructured":"Cardenas, C.E., Yang, J., Anderson, B.M., Court, L.E., Brock, K.B.: Advances in auto-segmentation. Semin. Radiat. Oncol. 29, 185\u2013197 (2019). https:\/\/doi.org\/10.1016\/j.semradonc.2019.02.001","journal-title":"Semin. Radiat. Oncol."},{"key":"10_CR13","unstructured":"Oreiller, V.A.V., Vallieres, M., Castelli, J., Boughdad, H.E.M.J.S., Adrien, J.O.P.: Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans (2020). http:\/\/proceedings.mlr.press\/v121\/andrearczyk20a.html"},{"key":"10_CR14","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.neucom.2018.10.099","volume":"392","author":"L Li","year":"2020","unstructured":"Li, L., Zhao, X., Lu, W., Tan, S.: Deep learning for variational multimodality tumor segmentation in PET\/CT. Neurocomputing 392, 277\u2013295 (2020)","journal-title":"Neurocomputing"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Leung, K.H., et al.: A physics-guided modular deep-learning based automated framework for tumor segmentation in PET images. arXiv Preprint arXiv:2002.07969 (2020)","DOI":"10.1088\/1361-6560\/ab8535"},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-020-6694-x","volume":"20","author":"K Kawauchi","year":"2020","unstructured":"Kawauchi, K., et al.: A convolutional neural network-based system to classify patients using FDG PET\/CT examinations. BMC Cancer 20, 1\u201310 (2020). https:\/\/doi.org\/10.1186\/s12885-020-6694-x","journal-title":"BMC Cancer"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Zhong, Z., et al.: 3D fully convolutional networks for co-segmentation of tumors on PET-CT images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 228\u2013231. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363561"},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1007\/s10278-020-00341-1","volume":"33","author":"S Jemaa","year":"2020","unstructured":"Jemaa, S., Fredrickson, J., Carano, R.A.D., Nielsen, T., de Crespigny, A., Bengtsson, T.: Tumor segmentation and feature extraction from whole-body FDG-PET\/CT using cascaded 2D and 3D convolutional neural networks. J. Digit. Imaging. 33, 888\u2013894 (2020). https:\/\/doi.org\/10.1007\/s10278-020-00341-1","journal-title":"J. Digit. Imaging."},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"15011","DOI":"10.1088\/1361-6560\/aaf44b","volume":"64","author":"X Zhao","year":"2018","unstructured":"Zhao, X., Li, L., Lu, W., Tan, S.: Tumor co-segmentation in PET\/CT using multi-modality fully convolutional neural network. Phys. Med. Biol. 64, 15011 (2018)","journal-title":"Phys. Med. Biol."},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Huang, B., et al.: Fully automated delineation of gross tumor volume for head and neck cancer on PET-CT using deep learning: a dual-center study. Contrast Media Mol. Imaging 2018, 1\u201312 (2018). https:\/\/pubmed.ncbi.nlm.nih.gov\/30473644\/","DOI":"10.1155\/2018\/8923028"},{"key":"10_CR21","unstructured":"Moe, Y.M., et al.: Deep learning for automatic tumour segmentation in PET\/CT images of patients with head and neck cancers. arXiv Preprint arXiv:1908.00841 (2019)"},{"key":"10_CR22","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TRPMS.2018.2890359","volume":"3","author":"Z Guo","year":"2019","unstructured":"Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans. Radiat. Plasma Med. Sci. 3, 162\u2013169 (2019)","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"10_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-030-32245-8_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"D Jin","year":"2019","unstructured":"Jin, D., et al.: Accurate esophageal gross tumor volume segmentation in PET\/CT using two-stream chained 3D deep network fusion. In: Shen, D., Liu, T., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 182\u2013191. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_21"},{"key":"10_CR24","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","volume":"3","author":"T Zhou","year":"2019","unstructured":"Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3, 100004 (2019)","journal-title":"Array"},{"key":"10_CR25","unstructured":"AIcrowd MICCAI 2020: HECKTOR Challenges. https:\/\/www.aicrowd.com\/challenges\/miccai-2020-hecktor. Accessed 07 Sept 2020"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET\/CT. In: Andrearczyk, V., et al. (eds.) HECKTOR 2020. LNCS, vol. 12603, pp. 1\u201321. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-67194-5_1"},{"key":"10_CR27","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 \u2014 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":"10_CR28","doi-asserted-by":"publisher","first-page":"103758","DOI":"10.1016\/j.compbiomed.2020.103758","volume":"121","author":"MA Naser","year":"2020","unstructured":"Naser, M.A., Deen, M.J.: Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput. Biol. Med. 121, 103758 (2020). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103758","journal-title":"Comput. Biol. Med."},{"key":"10_CR29","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv Preprint arXiv:1412.6806 (2014)"},{"key":"10_CR30","doi-asserted-by":"publisher","unstructured":"Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520\u20131528 (2015). https:\/\/doi.org\/10.1109\/ICCV.2015.178","DOI":"10.1109\/ICCV.2015.178"},{"key":"10_CR31","doi-asserted-by":"publisher","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10_CR32","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR33","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"10_CR34","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of ICML, p. 3 (2013)"},{"key":"10_CR35","unstructured":"Xu, B., Wang, N., Chen, T., Li, M.: Empirical evaluation of rectified activations in convolutional network. arXiv Preprint arXiv:1505.00853 (2015)"},{"key":"10_CR36","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.jmir.2016.11.003","volume":"48","author":"S Gudi","year":"2017","unstructured":"Gudi, S., et al.: Interobserver variability in the delineation of gross tumour volume and specified organs-at-risk during IMRT for head and neck cancers and the impact of FDG-PET\/CT on such variability at the primary site. J. Med. Imaging Radiat. Sci. 48, 184\u2013192 (2017). https:\/\/doi.org\/10.1016\/j.jmir.2016.11.003","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"10_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Cui, Z., Niu, X., Geng, S., Qiao, Y.: Image segmentation with pyramid dilated convolution based on ResNet and U-Net. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 364\u2013372. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70096-0_38","DOI":"10.1007\/978-3-319-70096-0_38"},{"key":"10_CR38","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"10_CR39","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (2017). https:\/\/openaccess.thecvf.com\/content_cvpr_2017_workshops\/w13\/html\/Jegou_The_One_Hundred_CVPR_2017_paper.html","DOI":"10.1109\/CVPRW.2017.156"}],"container-title":["Lecture Notes in Computer Science","Head and Neck Tumor Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67194-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T01:01:32Z","timestamp":1768179692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67194-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030671938","9783030671945"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67194-5_10","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 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HECKTOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3D Head and Neck Tumor Segmentation in PET\/CT Challenge","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hecktor2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aicrowd.com\/challenges\/miccai-2020-hecktor\/insights","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":"11","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":"2","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":"8","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":"18% - 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":"2.4","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":"1.7","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)"}},{"value":"The challenge was held virtually. One overview paper is also included.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}