{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:07:29Z","timestamp":1743001649115,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031120527"},{"type":"electronic","value":"9783031120534"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-12053-4_31","type":"book-chapter","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T09:15:50Z","timestamp":1658740550000},"page":"412-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Utility of\u00a0Equivariant Message Passing in\u00a0Cortical Mesh Segmentation"],"prefix":"10.1007","author":[{"given":"D\u00e1niel","family":"Unyi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ferdinando","family":"Insalata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petar","family":"Veli\u010dkovi\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B\u00e1lint","family":"Gyires-T\u00f3th","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"31_CR1","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health 1(6), e271\u2013e297 (2019)","DOI":"10.1016\/S2589-7500(19)30123-2"},{"key":"31_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fninf.2018.00023","volume":"12","author":"D Wen","year":"2018","unstructured":"Wen, D., Wei, Z., Zhou, Y., Li, G., Zhang, X., Han, W.: Deep learning methods to process fMRI data and their application in the diagnosis of cognitive impairment: a brief overview and our opinion. Front. Neuroinform. 12, 23 (2018)","journal-title":"Front. Neuroinform."},{"key":"31_CR3","doi-asserted-by":"crossref","unstructured":"Tahmassebi, A., Gandomi, A.H., McCann, I., Schulte, M.H., Goudriaan, A.E., Meyer-Baese, A.: Deep learning in medical imaging: fMRI big data analysis via convolutional neural networks. In: Proceedings of the Practice and Experience on Advanced Research Computing, pp. 1\u20134 (2018)","DOI":"10.1145\/3219104.3229250"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Cai, L., Gao, J., Zhao, D.: A review of the application of deep learning in medical image classification and segmentation. Annal. Transl. Med. 8(11) (2020)","DOI":"10.21037\/atm.2020.02.44"},{"key":"31_CR5","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":"31_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1007\/978-3-030-11726-9_32","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"W Chen","year":"2019","unstructured":"Chen, W., Liu, B., Peng, S., Sun, J., Qiao, X.: S3D-UNet: separable 3D U-Net for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 358\u2013368. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_32"},{"key":"31_CR7","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, 30 (2017)"},{"key":"31_CR8","unstructured":"Chen, J., et al.: TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"31_CR9","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. arXiv preprint arXiv:2105.05537 (2021)"},{"key":"31_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-030-87193-2_2","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Liu, H., Hu, Q.: TransFuse: fusing transformers and CNNs for medical image segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 14\u201324. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_2"},{"issue":"4","key":"31_CR11","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","volume":"21","author":"W Lorensen","year":"1987","unstructured":"Lorensen, W., Cline, H.: Marching cubes: a high resolution 3D surface construction algorithm. Comput. Graph. 21(4), 163\u2013169 (1987)","journal-title":"Comput. Graph."},{"key":"31_CR12","unstructured":"Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR, July 2017"},{"key":"31_CR13","unstructured":"Satorras, V.G., Hoogeboom, E., Welling, M.: E(n) equivariant graph neural networks. In: International Conference on Machine Learning, pp. 9323\u20139332. PMLR, July 2021"},{"key":"31_CR14","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)"},{"key":"31_CR15","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, 29 (2016)"},{"key":"31_CR16","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"31_CR17","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"issue":"1","key":"31_CR18","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Networks Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"31_CR19","unstructured":"Bronstein, M.M., Bruna, J., Cohen, T., Veli\u010dkovi\u0107, P.: Geometric deep learning: Grids, groups, graphs, geodesics, and gauges. arXiv preprint arXiv:2104.13478 (2021)"},{"key":"31_CR20","doi-asserted-by":"crossref","unstructured":"Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, T., Yeung, S.K.: Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1588\u20131597 (2019)","DOI":"10.1109\/ICCV.2019.00167"},{"issue":"1","key":"31_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2014.22","volume":"1","author":"R Ramakrishnan","year":"2014","unstructured":"Ramakrishnan, R., Dral, P.O., Rupp, M., Von Lilienfeld, O.A.: Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1(1), 1\u20137 (2014)","journal-title":"Sci. Data"},{"key":"31_CR22","unstructured":"Kipf, T., Fetaya, E., Wang, K.C., Welling, M., Zemel, R.: Neural relational inference for interacting systems. In: International Conference on Machine Learning, pp. 2688\u20132697. PMLR, July 2018"},{"key":"31_CR23","unstructured":"K\u00f6hler, J., Klein, L., & No\u00e9, F.: Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. arXiv preprint arXiv:1910.00753 (2019)"},{"key":"31_CR24","unstructured":"Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., Riley, P.: Tensor field networks: Rotation-and translation-equivariant neural networks for 3D point clouds. arXiv preprint arXiv:1802.08219 (2018)"},{"key":"31_CR25","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1109\/TPAMI.1987.4767965","volume":"5","author":"KS Arun","year":"1987","unstructured":"Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 5, 698\u2013700 (1987)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171\u2013178 (2016)","DOI":"10.1038\/nature18933"},{"key":"31_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117161","volume":"221","author":"J Cheng","year":"2020","unstructured":"Cheng, J., Dalca, A.V., Fischl, B., Z\u00f6llei, L., Initiative, A.D.N.: Cortical surface registration using unsupervised learning. Neuroimage 221, 117161 (2020)","journal-title":"Neuroimage"},{"key":"31_CR28","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/fninf.2018.00042","volume":"12","author":"SB Seong","year":"2018","unstructured":"Seong, S.B., Pae, C., Park, H.J.: Geometric convolutional neural network for analyzing surface-based neuroimaging data. Front. Neuroinform. 12, 42 (2018)","journal-title":"Front. Neuroinform."},{"key":"31_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1007\/978-3-030-20351-1_67","volume-title":"Information Processing in Medical Imaging","author":"F Zhao","year":"2019","unstructured":"Zhao, F., Xia, S., Wu, Z., Duan, D., Wang, L., Lin, W., Gilmore, J.H., Shen, D., Li, G.: Spherical U-Net on cortical surfaces: methods and applications. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 855\u2013866. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_67"},{"key":"31_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, F., et al.: Spherical deformable U-Net: application to cortical surface parcellation and development prediction. IEEE Trans. Med. Imaging 40(4), 1217\u20131228 (2021)","DOI":"10.1109\/TMI.2021.3050072"},{"key":"31_CR31","doi-asserted-by":"crossref","unstructured":"Fawaz, A., et al.: Benchmarking geometric deep learning for cortical segmentation and neurodevelopmental phenotype prediction. bioRxiv, 2021.12.01.470730 (2021)","DOI":"10.1101\/2021.12.01.470730"},{"key":"31_CR32","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.media.2019.03.012","volume":"54","author":"K Gopinath","year":"2019","unstructured":"Gopinath, K., Desrosiers, C., Lombaert, H.: Graph convolutions on spectral embeddings for cortical surface parcellation. Med. Image Anal. 54, 297\u2013305 (2019)","journal-title":"Med. Image Anal."},{"key":"31_CR33","unstructured":"Cucurull, G., et al.: Convolutional neural networks for mesh-based parcellation of the cerebral cortex. In: International Conference on Medical Imaging with Deep Learning (2018)"},{"key":"31_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/978-3-030-60365-6_15","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis","author":"K Gopinath","year":"2020","unstructured":"Gopinath, K., Desrosiers, C., Lombaert, H.: Graph domain adaptation for alignment-invariant brain surface segmentation. In: Sudre, C.H., Fehri, H., Arbel, T., Baumgartner, C.F., Dalca, A., Tanno, R., Van Leemput, K., Wells, W.M., Sotiras, A., Papiez, B., Ferrante, E., Parisot, S. (eds.) UNSURE\/GRAIL -2020. LNCS, vol. 12443, pp. 152\u2013163. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60365-6_15"},{"key":"31_CR35","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neuroimage.2016.09.069","volume":"170","author":"E Jakobsen","year":"2018","unstructured":"Jakobsen, E., Liem, F., Klados, M.A., Bayrak, \u015e, Petrides, M., Margulies, D.S.: Automated individual-level parcellation of Broca\u2019s region based on functional connectivity. Neuroimage 170, 41\u201353 (2018)","journal-title":"Neuroimage"},{"issue":"4","key":"31_CR36","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1111\/ejn.13140","volume":"43","author":"E Jakobsen","year":"2016","unstructured":"Jakobsen, E., B\u00f6ttger, J., Bellec, P., Geyer, S., R\u00fcbsamen, R., Petrides, M., Margulies, D.S.: Subdivision of Broca\u2019s region based on individual-level functional connectivity. Eur. J. Neurosci. 43(4), 561\u2013571 (2016)","journal-title":"Eur. J. Neurosci."},{"key":"31_CR37","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., Arbel, T., Carneiro, G., Syeda-Mahmood, T., Tavares, J.M.R.S., Moradi, M., Bradley, A., Greenspan, H., Papa, J.P., Madabhushi, A., Nascimento, J.C., Cardoso, J.S., Belagiannis, V., Lu, Z. (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":"31_CR38","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"6088","key":"31_CR39","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986)","journal-title":"Nature"},{"key":"31_CR40","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12053-4_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T08:07:24Z","timestamp":1707379644000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12053-4_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031120527","9783031120534"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12053-4_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miua2022.com\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}