{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:41:25Z","timestamp":1791916885396,"version":"4.3.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723896","type":"print"},{"value":"9783031723902","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72390-2_1","type":"book-chapter","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:03:14Z","timestamp":1729591394000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Biophysics Informed Pathological Regularisation for\u00a0Brain Tumour Segmentation"],"prefix":"10.1007","author":[{"given":"Lipei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanqi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carola-Bibiane","family":"Sch\u00f6nlieb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelica I","family":"Aviles-Rivero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,23]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955 (2018)","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"12","key":"1_CR2","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 transactions on pattern analysis and machine intelligence 39(12), 2481\u20132495 (2017)","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"1_CR3","unstructured":"Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F.C., Pati, S., et\u00a0al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Scientific data 4(1), 1\u201313 (2017)","journal-title":"Scientific data"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Carr\u00e9, A., Deutsch, E., Robert, C.: Automatic brain tumor segmentation with a bridge-unet deeply supervised enhanced with downsampling pooling combination, atrous spatial pyramid pooling, squeeze-and-excitation and evonorm. In: International MICCAI Brainlesion Workshop. pp. 253\u2013266. Springer (2021)","DOI":"10.1007\/978-3-031-09002-8_23"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. pp. 424\u2013432. Springer (2016)","DOI":"10.1007\/978-3-319-46723-8_49"},{"issue":"3","key":"1_CR7","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297\u2013302 (1945)","journal-title":"Ecology"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop. pp. 272\u2013284. Springer (2021)","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"issue":"9","key":"1_CR10","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using the hausdorff distance. IEEE Transactions on pattern analysis and machine intelligence 15(9), 850\u2013863 (1993)","journal-title":"IEEE Transactions on pattern analysis and machine intelligence"},{"issue":"2","key":"1_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods 18(2), 203\u2013211 (2021)","journal-title":"Nature methods"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"L\u00ea, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E.R., Batchelor, T., Unkelbach, J., Ayache, N.: Bayesian personalization of brain tumor growth model. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part II 18. pp. 424\u2013432. Springer (2015)","DOI":"10.1007\/978-3-319-24571-3_51"},{"issue":"9","key":"1_CR13","doi-asserted-by":"publisher","first-page":"2806","DOI":"10.1109\/TMI.2020.2975642","volume":"39","author":"L Liu","year":"2020","unstructured":"Liu, L., Hu, X., Zhu, L., Fu, C.W., Qin, J., Heng, P.A.: $$\\psi $$-net: Stacking densely convolutional lstms for sub-cortical brain structure segmentation. IEEE transactions on medical imaging 39(9), 2806\u20132817 (2020)","journal-title":"IEEE transactions on medical imaging"},{"key":"1_CR14","unstructured":"Liu, L., Huang, Z., Li\u00f2, P., Sch\u00f6nlieb, C.B., Aviles-Rivero, A.I.: Pc-swinmorph: Patch representation for unsupervised medical image registration and segmentation. arXiv preprint arXiv:2203.05684 (2022)"},{"key":"1_CR15","unstructured":"Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J.: On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265 (2019)"},{"issue":"6","key":"1_CR16","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","volume":"131","author":"DN Louis","year":"2016","unstructured":"Louis, D.N., Perry, A., Reifenberger, G., Von\u00a0Deimling, A., Figarella-Branger, D., Cavenee, W.K., Ohgaki, H., Wiestler, O.D., Kleihues, P., Ellison, D.W.: The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta neuropathologica 131(6), 803\u2013820 (2016)","journal-title":"Acta neuropathologica"},{"issue":"3","key":"1_CR17","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1093\/nop\/npac015","volume":"9","author":"JT Low","year":"2022","unstructured":"Low, J.T., Ostrom, Q.T., Cioffi, G., Neff, C., Waite, K.A., Kruchko, C., Barnholtz-Sloan, J.S.: Primary brain and other central nervous system tumors in the united states (2014-2018): A summary of the cbtrus statistical report for clinicians. Neuro-oncology practice 9(3), 165\u2013182 (2022)","journal-title":"Neuro-oncology practice"},{"key":"1_CR18","unstructured":"Ma, N., et\u00a0al.: Project-monai\/monai: 1.3. 0. zenodo (2023)"},{"key":"1_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2022.111342","volume":"557","author":"C Meaney","year":"2023","unstructured":"Meaney, C., Das, S., Colak, E., Kohandel, M.: Deep learning characterization of brain tumours with diffusion weighted imaging. Journal of Theoretical Biology 557, 111342 (2023)","journal-title":"Journal of Theoretical Biology"},{"key":"1_CR20","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 (3DV). pp. 565\u2013571. Ieee (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3d mri brain tumor segmentation using autoencoder regularization. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4. pp. 311\u2013320. Springer (2019)","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"1_CR22","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et\u00a0al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Ragoza, M., Batmanghelich, K.: Physics-informed neural networks for tissue elasticity reconstruction in magnetic resonance elastography. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 333\u2013343. Springer (2023)","DOI":"10.1007\/978-3-031-43999-5_32"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Shanmugam, D., Blalock, D., Balakrishnan, G., Guttag, J.: Better aggregation in test-time augmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 1214\u20131223 (2021)","DOI":"10.1109\/ICCV48922.2021.00125"},{"key":"1_CR27","first-page":"7462","volume":"33","author":"V Sitzmann","year":"2020","unstructured":"Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. Advances in neural information processing systems 33, 7462\u20137473 (2020)","journal-title":"Advances in neural information processing systems"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Song, T.A., Chowdhury, S.R., Yang, F., Jacobs, H.I., Sepulcre, J., Wedeen, V.J., Johnson, K.A., Dutta, J.: A physics-informed geometric learning model for pathological tau spread in alzheimer\u2019s disease. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VII 23. pp. 418\u2013427. Springer (2020)","DOI":"10.1007\/978-3-030-59728-3_41"},{"key":"1_CR29","unstructured":"Zhang, M., Lucas, J., Ba, J., Hinton, G.E.: Lookahead optimizer: k steps forward, 1 step back. Advances in neural information processing systems 32 (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72390-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:03:41Z","timestamp":1729591421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72390-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723896","9783031723902"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72390-2_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}