{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:06:54Z","timestamp":1743106014543,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164422"},{"type":"electronic","value":"9783031164439"}],"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\/978-3-031-16443-9_52","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"538-548","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and No Retraining"],"prefix":"10.1007","author":[{"given":"Benjamin","family":"Billot","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colin","family":"Magdamo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven E.","family":"Arnold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeshna","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Eugenio","family":"Iglesias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A.: TensorFlow: a system for large-scale machine learning. In: Symposium on Operating Systems Design and Implementation, pp. 265\u2013283 (2016)","key":"52_CR1"},{"issue":"4","key":"52_CR2","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449\u2013459 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9983-4","journal-title":"J. Digit. Imaging"},{"key":"52_CR3","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","volume":"166","author":"F Alfaro-Almagro","year":"2018","unstructured":"Alfaro-Almagro, F., Jenkinson, M., Bangerter, N., Andersson, J., Griffanti, L., Douaud, G., et al.: Image processing and quality control for the first 10,000 brain imaging datasets from UK biobank. Neuroimage 166, 400\u2013424 (2018)","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: Medical Image Computing and Computer Assisted Intervention, pp. 924\u2013931 (2006)","key":"52_CR4","DOI":"10.1007\/11866565_113"},{"issue":"3","key":"52_CR5","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neuroimage.2005.02.018","volume":"26","author":"J Ashburner","year":"2005","unstructured":"Ashburner, J., Friston, K.: Unified segmentation. Neuroimage 26(3), 39\u201351 (2005)","journal-title":"Neuroimage"},{"unstructured":"Billot, B., et al.: SynthSeg: domain randomisation for segmentation of brain scans of any contrast and resolution. arXiv:2107.09559 [cs] (2021)","key":"52_CR6"},{"unstructured":"Billot, B., Greve, D., Van Leemput, K., Fischl, B., Iglesias, J.E., Dalca, A.: A learning strategy for contrast-agnostic MRI segmentation. In: Medical Imaging with Deep Learning, pp. 75\u201393 (2020)","key":"52_CR7"},{"key":"52_CR8","first-page":"1190","volume":"16","author":"R Byrd","year":"1995","unstructured":"Byrd, R., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. J. Sci. Comput. 16, 1190\u20131208 (1995)","journal-title":"J. Sci. Comput."},{"key":"52_CR9","first-page":"65","volume":"33","author":"C Chen","year":"2019","unstructured":"Chen, C., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Synergistic image and feature adaptation: towards cross-modality domain adaptation for medical image segmentation. AAAI Conf. Artif. Intell. 33, 65\u201372 (2019)","journal-title":"AAAI Conf. Artif. Intell."},{"key":"52_CR10","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1109\/42.97590","volume":"10","author":"H Choi","year":"1991","unstructured":"Choi, H., Haynor, D., Kim, Y.: Partial volume tissue classification of multichannel magnetic resonance images-a mixel model. IEEE Trans. Med. Imaging 10, 395\u2013407 (1991)","journal-title":"IEEE Trans. Med. Imaging"},{"unstructured":"Chollet, F.: Keras (2015). https:\/\/keras.io","key":"52_CR11"},{"unstructured":"Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv:1511.07289 [cs] (2016)","key":"52_CR12"},{"issue":"11","key":"52_CR13","doi-asserted-by":"publisher","first-page":"5501","DOI":"10.1002\/hbm.23743","volume":"38","author":"P Coup\u00e9","year":"2017","unstructured":"Coup\u00e9, P., Catheline, G., Lanuza, E., Manj\u00f3n, J.: Towards a unified analysis of brain maturation and aging across the entire lifespan: a MRI analysis. Hum. Brain Mapp. 38(11), 5501\u20135518 (2017)","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"52_CR14","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1002\/hbm.25320","volume":"43","author":"D Dima","year":"2022","unstructured":"Dima, D., Modabbernia, A., Papachristou, E., Doucet, G.E., et al.: Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3\u201390 years. Hum. Brain Mapp. 43(1), 452\u2013469 (2022)","journal-title":"Hum. Brain Mapp."},{"key":"52_CR15","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: FreeSurfer. NeuroImage 62, 774\u2013781 (2012)","journal-title":"NeuroImage"},{"key":"52_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/S0896-6273(02)00569-X","volume":"33","author":"B Fischl","year":"2002","unstructured":"Fischl, B., Salat, D., Busa, E., Albert, M., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 41\u201355 (2002)","journal-title":"Neuron"},{"key":"52_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/978-3-319-66179-7_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2014 MICCAI 2017","author":"M Ghafoorian","year":"2017","unstructured":"Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516\u2013524. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_59"},{"key":"52_CR18","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1038\/nature14101","volume":"520","author":"D Hibar","year":"2015","unstructured":"Hibar, D., Stein, J., Renteria, M., Arias-Vasquez, A., et al.: Common genetic variants influence human subcortical brain structures. Nature 520, 224\u2013229 (2015)","journal-title":"Nature"},{"issue":"2","key":"52_CR19","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., Kohl, S., Petersen, J., Maier-Hein, K.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"4","key":"52_CR20","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack","year":"2008","unstructured":"Jack, C.R., Bernstein, M., Fox, N., Thompson, P., Alexander, G., Harvey, D., et al.: The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging JMRI 27(4), 685\u2013691 (2008)","journal-title":"J. Magn. Reson. Imaging JMRI"},{"key":"52_CR21","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.neuroimage.2019.05.033","volume":"199","author":"A Jog","year":"2019","unstructured":"Jog, A., Hoopes, A., Greve, D., Van Leemput, K., Fischl, B.: PSACNN: pulse sequence adaptive fast whole brain segmentation. Neuroimage 199, 553\u2013569 (2019)","journal-title":"Neuroimage"},{"key":"52_CR22","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., et al.: 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":"52_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101907","volume":"68","author":"N Karani","year":"2021","unstructured":"Karani, N., Erdil, E., Chaitanya, K., Konukoglu, E.: Test-time adaptable neural networks for robust medical image segmentation. Med. Image Anal. 68, 101907 (2021)","journal-title":"Med. Image Anal."},{"key":"52_CR24","doi-asserted-by":"publisher","first-page":"83002","DOI":"10.1109\/ACCESS.2021.3086530","volume":"9","author":"M Khan","year":"2021","unstructured":"Khan, M., Gajendran, M., Lee, Y., Khan, M.: Deep neural architectures for medical image semantic segmentation: review. IEEE Access 9, 83002\u201383024 (2021)","journal-title":"IEEE Access"},{"unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (2017)","key":"52_CR25"},{"key":"52_CR26","doi-asserted-by":"publisher","first-page":"6742","DOI":"10.1038\/s41598-019-43299-z","volume":"9","author":"K Kushibar","year":"2019","unstructured":"Kushibar, K., et al.: Supervised domain adaptation for automatic sub-cortical brain structure segmentation with minimal user interaction. Sci. Rep. 9, 6742 (2019)","journal-title":"Sci. Rep."},{"issue":"12","key":"52_CR27","doi-asserted-by":"publisher","first-page":"3813","DOI":"10.1109\/TMI.2020.3005297","volume":"39","author":"A Larrazabal","year":"2020","unstructured":"Larrazabal, A., Mart\u00ednez, C., Glocker, B., Ferrante, E.: Post-DAE: anatomically plausible segmentation via post-processing with denoising autoencoders. IEEE Trans. Med. Imaging 39(12), 3813\u20133820 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision, pp. 565\u2013571 (2016)","key":"52_CR28","DOI":"10.1109\/3DV.2016.79"},{"unstructured":"Nosrati, M., Hamarneh, G.: Incorporating prior knowledge in medical image segmentation: a survey. arXiv:1607.01092 [cs] (2016)","key":"52_CR29"},{"issue":"2","key":"52_CR30","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2018","unstructured":"Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384\u2013395 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"52_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-030-33391-1_7","volume-title":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data","author":"M Orbes-Arteaga","year":"2019","unstructured":"Orbes-Arteaga, M., et al.: Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. In: Wang, Q., et al. (eds.) DART\/MIL3ID -2019. LNCS, vol. 11795, pp. 54\u201362. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33391-1_7"},{"issue":"3","key":"52_CR32","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1001\/jama.2018.20295","volume":"321","author":"O Oren","year":"2019","unstructured":"Oren, O., Kebebew, E., Ioannidis, J.: Curbing unnecessary and wasted diagnostic imaging. JAMA 321(3), 245\u2013246 (2019)","journal-title":"JAMA"},{"key":"52_CR33","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.neuroimage.2016.09.011","volume":"143","author":"O Puonti","year":"2016","unstructured":"Puonti, O., Iglesias, J.E., Van Leemput, K.: Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling. Neuroimage 143, 235\u2013249 (2016)","journal-title":"Neuroimage"},{"key":"52_CR34","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":"52_CR35","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","volume":"66","author":"HR Roth","year":"2018","unstructured":"Roth, H.R., Oda, H., Zhou, X., Shimizu, N., Yang, Y., Hayashi, Y., et al.: An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput. Med. Imaging Graph. 66, 90\u201399 (2018)","journal-title":"Comput. Med. Imaging Graph."},{"doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., Abbeel, P.: Domain randomization for transferring deep neural networks from simulation to the real world. In: International Conference on Intelligent Robots, pp. 23\u201330 (2017)","key":"52_CR36","DOI":"10.1109\/IROS.2017.8202133"},{"issue":"4","key":"52_CR37","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","volume":"62","author":"D Van Essen","year":"2012","unstructured":"Van Essen, D., Ugurbil, K., Auerbach, E., et al.: The human connectome project: a data acquisition perspective. Neuroimage 62(4), 2222\u20132231 (2012)","journal-title":"Neuroimage"},{"key":"52_CR38","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/TMI.2002.806587","volume":"22","author":"K Van Leemput","year":"2003","unstructured":"Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: A unifying framework for partial volume segmentation of brain MR images. IEEE Trans. Med. Imaging 22, 105\u2013119 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"52_CR39","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Wang, X., Yang, D., Roth, H., Xu, D., Xu, Z., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531\u20132540 (2020)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16443-9_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:48:40Z","timestamp":1709830120000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16443-9_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164422","9783031164439"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16443-9_52","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":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}