{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:07:56Z","timestamp":1770754076590,"version":"3.50.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030603649","type":"print"},{"value":"9783030603656","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-60365-6_17","type":"book-chapter","created":{"date-parts":[[2020,10,5]],"date-time":"2020-10-05T06:05:01Z","timestamp":1601877901000},"page":"174-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface"],"prefix":"10.1007","author":[{"given":"Vitalis","family":"Vosylius","sequence":"first","affiliation":[]},{"given":"Andy","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Cemlyn","family":"Waters","sequence":"additional","affiliation":[]},{"given":"Alexey","family":"Zakharov","sequence":"additional","affiliation":[]},{"given":"Francis","family":"Ward","sequence":"additional","affiliation":[]},{"given":"Loic","family":"Le Folgoc","sequence":"additional","affiliation":[]},{"given":"John","family":"Cupitt","sequence":"additional","affiliation":[]},{"given":"Antonios","family":"Makropoulos","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Schuh","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Alansary","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,5]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Besson, P., Parrish, T., Katsaggelos, A.K., Bandt, S.K.: Geometric deep learning on brain shape predicts sex and age. BioRxiv (2020)","DOI":"10.1101\/2020.06.29.177543"},{"issue":"4","key":"17_CR2","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.bpobgyn.2009.01.011","volume":"23","author":"C Bottomley","year":"2009","unstructured":"Bottomley, C., Bourne, T.: Dating and growth in the first trimester. Best Pract. Res. Clin. Obstet. Gynaecol. 23(4), 439\u2013452 (2009)","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"issue":"1","key":"17_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"17_CR4","unstructured":"Brock, A., Lim, T., Ritchie, J.M., Weston, N.: Generative and discriminative voxel modeling with convolutional neural networks. arXiv preprint arXiv:1608.04236 (2016)"},{"issue":"4","key":"17_CR5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Sig. Process. Mag. 34(4), 18\u201342 (2017)","journal-title":"IEEE Sig. Process. Mag."},{"key":"17_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1007\/978-3-319-66182-7_10","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2017","author":"CJ Brown","year":"2017","unstructured":"Brown, C.J., et al.: Prediction of brain network age and factors of\u00a0delayed maturation in very preterm infants. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 84\u201391. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_10"},{"key":"17_CR7","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)"},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2017.07.059","volume":"163","author":"JH Cole","year":"2017","unstructured":"Cole, J.H., et al.: Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. NeuroImage 163, 115\u2013124 (2017)","journal-title":"NeuroImage"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Deprez, M., Wang, S., Ledig, C., Hajnal, J.V., Counsell, S.J., Schnabel, J.A.: Segmentation of myelin-like signals on clinical MR images for age estimation in preterm infants. bioRxiv, p. 357749 (2018)","DOI":"10.1101\/357749"},{"issue":"5","key":"17_CR10","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1542\/peds.2004-1915","volume":"114","author":"WA Engle","year":"2004","unstructured":"Engle, W.A.: Age terminology during the perinatal period. Pediatrics 114(5), 1362\u20131364 (2004)","journal-title":"Pediatrics"},{"key":"17_CR11","unstructured":"Fetit, A.E., et al.: A deep learning approach to segmentation of the developing cortex in fetal brain mri with minimal manual labeling. In: MIDL (2020)"},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"102195","DOI":"10.1016\/j.nicl.2020.102195","volume":"25","author":"P Galdi","year":"2020","unstructured":"Galdi, P., et al.: Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth. NeuroImage Clin. 25, 102195 (2020)","journal-title":"NeuroImage Clin."},{"key":"17_CR13","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, pp. 249\u2013256 (2010)"},{"key":"17_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/978-3-030-00931-1_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"B Guti\u00e9rrez-Becker","year":"2018","unstructured":"Guti\u00e9rrez-Becker, B., Wachinger, C.: Deep multi-structural shape analysis: application to neuroanatomy. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 523\u2013531. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_60"},{"issue":"4","key":"17_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3322959","volume":"38","author":"R Hanocka","year":"2019","unstructured":"Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D.: MeshCNN: a network with an edge. ACM TOG 38(4), 1\u201312 (2019)","journal-title":"ACM TOG"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"17_CR17","unstructured":"Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)"},{"issue":"1","key":"17_CR18","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1109\/JBHI.2019.2897020","volume":"24","author":"D Hu","year":"2019","unstructured":"Hu, D., Wu, Z., Lin, W., Li, G., Shen, D.: Hierarchical rough-to-fine model for infant age prediction based on cortical features. IEEE J. Biomed. Health Inf. 24(1), 214\u2013225 (2019)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"17_CR19","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)"},{"key":"17_CR20","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.3389\/fneur.2019.01346","volume":"10","author":"H Jiang","year":"2019","unstructured":"Jiang, H., et al.: Predicting brain age of healthy adults based on structural MRI parcellation using convolutional neural networks. Front. Neurol. 10, 1346 (2019)","journal-title":"Front. Neurol."},{"key":"17_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61\u201378 (2017)","journal-title":"Med. Image Anal."},{"key":"17_CR22","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"17_CR23","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neuroimage.2018.01.054","volume":"173","author":"A Makropoulos","year":"2018","unstructured":"Makropoulos, A.: The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 173, 88\u2013112 (2018)","journal-title":"Neuroimage"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Masci, J., Boscaini, D., Bronstein, M., Vandergheynst, P.: Geodesic convolutional neural networks on Riemannian manifolds. In: ICCV Workshops, pp. 37\u201345 (2015)","DOI":"10.1109\/ICCVW.2015.112"},{"issue":"10","key":"17_CR25","doi-asserted-by":"publisher","first-page":"4681","DOI":"10.1073\/pnas.1812156116","volume":"116","author":"M Ouyang","year":"2019","unstructured":"Ouyang, M.: Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging. Proc. Nat. Acad. Sci. 116(10), 4681\u20134688 (2019)","journal-title":"Proc. Nat. Acad. Sci."},{"issue":"1","key":"17_CR26","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1002\/uog.3909","volume":"29","author":"D Paladini","year":"2007","unstructured":"Paladini, D., Malinger, G., Monteagudo, A., Pilu, G., Timor-Tritsch, I., Toi, A.: Sonographic examination of the fetal central nervous system: guidelines for performing the \u2018basic examination\u2019 and the \u2018fetal neurosonogram\u2019. Ultrasound Obstet. Gynecol. 29(1), 109\u2013116 (2007)","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"17_CR27","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: CVPR, pp. 652\u2013660 (2017)"},{"key":"17_CR28","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. 5099\u20135108 (2017)"},{"key":"17_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/978-3-319-46720-7_25","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"I Rekik","year":"2016","unstructured":"Rekik, I., Li, G., Yap, P.-T., Chen, G., Lin, W., Shen, D.: A hybrid multishape learning framework for longitudinal prediction of cortical surfaces and fiber tracts using neonatal data. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 210\u2013218. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46720-7_25"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Schuh, A., et al.: A deformable model for the reconstruction of the neonatal cortex. In: ISBI, pp. 800\u2013803. IEEE (2017)","DOI":"10.1109\/ISBI.2017.7950639"},{"key":"17_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-642-33418-4_26","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012","author":"M Toews","year":"2012","unstructured":"Toews, M., Wells, W.M., Z\u00f6llei, L.: A feature-based developmental model of the infant brain in structural MRI. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, pp. 204\u2013211. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33418-4_26"},{"key":"17_CR32","unstructured":"Wu, Z., et al.: 3D shapeNets: a deep representation for volumetric shapes. In: CVPR, pp. 1912\u20131920 (2015)"}],"container-title":["Lecture Notes in Computer Science","Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60365-6_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T22:02:23Z","timestamp":1759615343000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60365-6_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030603649","9783030603656"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60365-6_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GRAIL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Graphs in Biomedical Image Analysis","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":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"grail2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/grail-miccai.github.io\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","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":"10","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":"83% - 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":"2","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 workshop was held virtually due to the COVID-19 pandemic.","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)"}}]}}