{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:19:35Z","timestamp":1769843975699,"version":"3.49.0"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439926","type":"print"},{"value":"9783031439933","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_40","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"409-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["TractCloud: Registration-Free Tractography Parcellation with\u00a0a\u00a0Novel Local-Global Streamline Point Cloud Representation"],"prefix":"10.1007","author":[{"given":"Tengfei","family":"Xue","sequence":"first","affiliation":[]},{"given":"Yuqian","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chaoyi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Alexandra J.","family":"Golby","sequence":"additional","affiliation":[]},{"given":"Nikos","family":"Makris","sequence":"additional","affiliation":[]},{"given":"Yogesh","family":"Rathi","sequence":"additional","affiliation":[]},{"given":"Weidong","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lauren J.","family":"O\u2019Donnell","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"40_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/978-3-030-59728-3_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"P Astolfi","year":"2020","unstructured":"Astolfi, P., et al.: Tractogram filtering of anatomically non-plausible fibers with geometric deep learning. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 291\u2013301. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_29"},{"issue":"4","key":"40_CR2","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1002\/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O","volume":"44","author":"PJ Basser","year":"2000","unstructured":"Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44(4), 625\u2013632 (2000)","journal-title":"Magn. Reson. Med."},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J.: PointNet: Deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77\u201385 (2017)","DOI":"10.1109\/CVPR.2017.16"},{"key":"40_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Y., et al.: White matter tracts are point clouds: neuropsychological score prediction and critical region localization via geometric deep learning. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science. vol. 13431. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_17","DOI":"10.1007\/978-3-031-16431-6_17"},{"key":"40_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120086","volume":"273","author":"Y Chen","year":"2023","unstructured":"Chen, Y., et al.: Deep fiber clustering: anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation. Neuroimage 273, 120086 (2023)","journal-title":"Neuroimage"},{"key":"40_CR6","unstructured":"Chen, Y., et al.: TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance. arXiv 2307.0398 (2023)"},{"key":"40_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/978-3-030-87234-2_47","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Chen","year":"2021","unstructured":"Chen, Y., et al.: Deep fiber clustering: anatomically informed unsupervised deep learning for fast and effective white matter parcellation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 497\u2013507. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_47"},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.nicl.2017.07.020","volume":"16","author":"M Cousineau","year":"2017","unstructured":"Cousineau, M., et al.: A test-retest study on parkinson\u2019s PPMI dataset yields statistically significant white matter fascicles. Neuroimage Clin. 16, 222\u2013233 (2017)","journal-title":"Neuroimage Clin."},{"key":"40_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.886772","volume":"16","author":"AD Edwards","year":"2022","unstructured":"Edwards, A.D., et al.: The developing human connectome project neonatal data release. Front. Neurosci. 16, 886772 (2022)","journal-title":"Front. Neurosci."},{"issue":"9","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323\u20131341 (2012)","journal-title":"Magn. Reson. Imaging"},{"key":"40_CR11","doi-asserted-by":"publisher","first-page":"175","DOI":"10.3389\/fnins.2012.00175","volume":"6","author":"E Garyfallidis","year":"2012","unstructured":"Garyfallidis, E., et al.: QuickBundles, a method for tractography simplification. Front. Neurosci. 6, 175 (2012)","journal-title":"Front. Neurosci."},{"key":"40_CR12","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.neuroimage.2017.07.015","volume":"170","author":"E Garyfallidis","year":"2018","unstructured":"Garyfallidis, E., et al.: Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 170, 283\u2013295 (2018)","journal-title":"Neuroimage"},{"key":"40_CR13","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.neuroimage.2015.05.016","volume":"117","author":"E Garyfallidis","year":"2015","unstructured":"Garyfallidis, E., Ocegueda, O., Wassermann, D., Descoteaux, M.: Robust and efficient linear registration of white-matter fascicles in the space of streamlines. Neuroimage 117, 124\u2013140 (2015)","journal-title":"Neuroimage"},{"key":"40_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1007\/978-3-319-66182-7_63","volume-title":"Medical Image Computing and Computer Assisted Intervention-MICCAI 2017","author":"V Gupta","year":"2017","unstructured":"Gupta, V., Thomopoulos, S.I., Rashid, F.M., Thompson, P.M.: FiberNET: an ensemble deep learning framework for clustering white matter fibers. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 548\u2013555. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_63"},{"key":"40_CR15","doi-asserted-by":"publisher","unstructured":"Kumaralingam, L., Thanikasalam, K., Sotheeswaran, S., Mahadevan, J., Ratnarajah, N.: Segmentation of whole-brain tractography: a deep learning algorithm based on 3D raw curve points. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science. vol. 13431. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_18","DOI":"10.1007\/978-3-031-16431-6_18"},{"key":"40_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102126","volume":"72","author":"JH Legarreta","year":"2021","unstructured":"Legarreta, J.H., et al.: Filtering in tractography using autoencoders (FINTA). Med. Image Anal. 72, 102126 (2021)","journal-title":"Med. Image Anal."},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Legarreta, J.H., et al.: Clustering in tractography using autoencoders (CINTA). In: Computational Diffusion MRI, pp. 125\u2013136 (2022)","DOI":"10.1007\/978-3-031-21206-2_11"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: DeepRGVP: A novel Microstructure-Informed supervised contrastive learning framework for automated identification of the retinogeniculate pathway using dMRI tractography. In: ISBI (2023)","DOI":"10.1109\/ISBI53787.2023.10230833"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Liu, F., et al.: DeepBundle: fiber bundle parcellation with graph convolution neural networks. In: Graph Learning in Medical Imaging, pp. 88\u201395 (2019)","DOI":"10.1007\/978-3-030-35817-4_11"},{"key":"40_CR20","doi-asserted-by":"publisher","unstructured":"Liu, W., Lu, Q., Zhuo, Z., Liu, Y., Ye, C.: One-shot segmentation of novel white matter tracts via extensive data augmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention-MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science. vol. 13431. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_13","DOI":"10.1007\/978-3-031-16431-6_13"},{"key":"40_CR21","unstructured":"Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: a simple residual MLP framework. In: International Conference on Learning Representations (ICLR) (2022)"},{"issue":"9","key":"40_CR22","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TMI.2010.2048121","volume":"29","author":"JG Malcolm","year":"2010","unstructured":"Malcolm, J.G., et al.: Filtered multitensor tractography. IEEE Trans. Med. Imaging 29(9), 1664\u20131675 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"40_CR23","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.pneurobio.2011.09.005","volume":"95","author":"K Marek","year":"2011","unstructured":"Marek, K., et al.: The parkinson progression marker initiative (PPMI). Prog. Neurobiol. 95(4), 629\u2013635 (2011)","journal-title":"Prog. Neurobiol."},{"key":"40_CR24","unstructured":"Ngattai Lam, P.D., et al.: TRAFIC: Fiber tract classification using deep learning. Proc. SPIE Int. Soc. Opt. Eng. 10574, 1057412 (2018)"},{"issue":"21","key":"40_CR25","doi-asserted-by":"publisher","first-page":"e101","DOI":"10.1158\/0008-5472.CAN-17-0332","volume":"77","author":"I Norton","year":"2017","unstructured":"Norton, I., et al.: SlicerDMRI: open source diffusion MRI software for brain cancer research. Cancer Res. 77(21), e101\u2013e103 (2017)","journal-title":"Cancer Res."},{"key":"40_CR26","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: NeurIPS, pp. 5105\u20135114 (2017)"},{"key":"40_CR27","doi-asserted-by":"publisher","first-page":"166","DOI":"10.3389\/fnins.2016.00166","volume":"10","author":"CP Reddy","year":"2016","unstructured":"Reddy, C.P., Rathi, Y.: Joint Multi-Fiber NODDI parameter estimation and tractography using the unscented information filter. Front. Neurosci. 10, 166 (2016)","journal-title":"Front. Neurosci."},{"key":"40_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119550","volume":"262","author":"C Rom\u00e1n","year":"2022","unstructured":"Rom\u00e1n, C., et al.: Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data. Neuroimage 262, 119550 (2022)","journal-title":"Neuroimage"},{"key":"40_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116703","volume":"214","author":"V Siless","year":"2020","unstructured":"Siless, V., et al.: Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan. Neuroimage 214, 116703 (2020)","journal-title":"Neuroimage"},{"key":"40_CR30","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"DC Van Essen","year":"2013","unstructured":"Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","journal-title":"Neuroimage"},{"key":"40_CR31","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.dcn.2017.10.002","volume":"32","author":"ND Volkow","year":"2018","unstructured":"Volkow, N.D., et al.: The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32, 4\u20137 (2018)","journal-title":"Dev. Cogn. Neurosci."},{"issue":"5","key":"40_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3326362","volume":"38","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38(5), 1\u201312 (2019)","journal-title":"ACM Trans. Graph."},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Accurate corresponding fiber tract segmentation via FiberGeoMap learner. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 143\u2013152 (2022)","DOI":"10.1007\/978-3-031-16431-6_14"},{"key":"40_CR34","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neuroimage.2018.07.070","volume":"183","author":"J Wasserthal","year":"2018","unstructured":"Wasserthal, J., Neher, P., Maier-Hein, K.H.: TractSeg - fast and accurate white matter tract segmentation. Neuroimage 183, 239\u2013253 (2018)","journal-title":"Neuroimage"},{"key":"40_CR35","doi-asserted-by":"crossref","unstructured":"Xiang, T., Zhang, C., Song, Y., Yu, J., Cai, W.: Walk in the cloud: learning curves for point clouds shape analysis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00095"},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: A registration- and uncertainty-based framework for white matter tract segmentation with only one annotated subject. In: ISBI (2023)","DOI":"10.1109\/ISBI53787.2023.10230415"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Objective detection of eloquent axonal pathways to minimize postoperative deficits in pediatric epilepsy surgery using diffusion tractography and convolutional neural networks. IEEE Trans. Med. Imaging 38(8), 1910\u20131922 (2019)","DOI":"10.1109\/TMI.2019.2902073"},{"key":"40_CR38","doi-asserted-by":"crossref","unstructured":"Xue, T., et al.: SupWMA: Consistent and efficient tractography parcellation of superficial white matter with deep learning. In: ISBI (2022)","DOI":"10.1109\/ISBI52829.2022.9761541"},{"key":"40_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102759","volume":"85","author":"T Xue","year":"2023","unstructured":"Xue, T., et al.: Superficial white matter analysis: an efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions. Med. Image Anal. 85, 102759 (2023)","journal-title":"Med. Image Anal."},{"key":"40_CR40","doi-asserted-by":"crossref","unstructured":"Yan, X., et al.: PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00563"},{"key":"40_CR41","unstructured":"Yu, J., et al.: 3D medical point transformer: Introducing convolution to attention networks for medical point cloud analysis. arXiv 2112.04863 (2021)"},{"key":"40_CR42","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.neuroimage.2018.06.027","volume":"179","author":"F Zhang","year":"2018","unstructured":"Zhang, F., et al.: An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 179, 429\u2013447 (2018)","journal-title":"Neuroimage"},{"issue":"10","key":"40_CR43","doi-asserted-by":"publisher","first-page":"3041","DOI":"10.1002\/hbm.24579","volume":"40","author":"F Zhang","year":"2019","unstructured":"Zhang, F., et al.: Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Hum. Brain Mapp. 40(10), 3041\u20133057 (2019)","journal-title":"Hum. Brain Mapp."},{"key":"40_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101761","volume":"65","author":"F Zhang","year":"2020","unstructured":"Zhang, F., et al.: Deep white matter analysis (DeepWMA): fast and consistent tractography segmentation. Med. Image Anal. 65, 101761 (2020)","journal-title":"Med. Image Anal."},{"key":"40_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118870","volume":"249","author":"F Zhang","year":"2022","unstructured":"Zhang, F., et al.: Quantitative mapping of the brain\u2019s structural connectivity using diffusion MRI tractography: a review. Neuroimage 249, 118870 (2022)","journal-title":"Neuroimage"},{"key":"40_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, H., et al.: Point transformer. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.01595"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:10:21Z","timestamp":1712074221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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":"5","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)"}}]}}