{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:03:53Z","timestamp":1779174233808,"version":"3.51.4"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030117252","type":"print"},{"value":"9783030117269","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-11726-9_29","type":"book-chapter","created":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T13:24:06Z","timestamp":1548422646000},"page":"321-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation"],"prefix":"10.1007","author":[{"given":"Mina","family":"Rezaei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christoph","family":"Meinel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,1,26]]},"reference":[{"key":"29_CR1","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265\u2013283 (2016)"},{"issue":"2","key":"29_CR2","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1109\/TMI.2013.2287793","volume":"33","author":"M Afshin","year":"2014","unstructured":"Afshin, M., et al.: Regional assessment of cardiac left ventricular myocardial function via MRI statistical features. IEEE Trans. Med. Imaging 33(2), 481\u2013494 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"29_CR3","unstructured":"Bakas, S., Akbari, H.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. 286 (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"key":"29_CR4","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. 286 (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"170117","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"Spyridon Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data (2017). https:\/\/doi.org\/10.1038\/sdata.2017.117","journal-title":"Scientific Data"},{"key":"29_CR6","unstructured":"Bakas, S., Reyes, M., Menze, B. et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)"},{"key":"29_CR7","unstructured":"Chollet, F., et al.: Keras (2015)"},{"key":"29_CR8","unstructured":"Christ, P.F., et al.: Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. CoRR abs\/1702.05970 (2017). http:\/\/arxiv.org\/abs\/1702.05970"},{"key":"29_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-642-21073-0_38","volume-title":"Combinatorial Image Analysis","author":"M Ciecholewski","year":"2011","unstructured":"Ciecholewski, M.: Support vector machine approach to cardiac SPECT diagnosis. In: Aggarwal, J.K., Barneva, R.P., Brimkov, V.E., Koroutchev, K.N., Korutcheva, E.R. (eds.) IWCIA 2011. LNCS, vol. 6636, pp. 432\u2013443. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21073-0_38"},{"key":"29_CR10","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.030","volume":"91","author":"G Douzas","year":"2018","unstructured":"Douzas, G., Bacao, F.: Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst. Appl. 91, 464\u2013471 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"29_CR11","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.media.2012.10.005","volume":"17","author":"A Eslami","year":"2013","unstructured":"Eslami, A., Karamalis, A., Katouzian, A., Navab, N.: Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI. Med. Image Anal. 17(2), 236\u2013253 (2013)","journal-title":"Med. Image Anal."},{"key":"29_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/978-3-319-75238-9_6","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"L Fidon","year":"2018","unstructured":"Fidon, L., et al.: Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 64\u201376. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_6"},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"S69","DOI":"10.1016\/j.neuroimage.2004.07.016","volume":"23","author":"B Fischl","year":"2004","unstructured":"Fischl, B., et al.: Sequence-independent segmentation of magnetic resonance images. Neuroimage 23, S69\u2013S84 (2004)","journal-title":"Neuroimage"},{"key":"29_CR14","unstructured":"Gholami, A., et al.: A novel domain adaptation framework for medical image segmentation. CoRR abs\/1810.05732 (2018). http:\/\/arxiv.org\/abs\/org\/abs\/1810.05732"},{"key":"29_CR15","unstructured":"Goodfellow, I.J., et al.: Generative Adversarial Networks. ArXiv e-prints (2014)"},{"key":"29_CR16","unstructured":"Hashemi, S.R., Salehi, S.S.M., Erdogmus, D., Prabhu, S.P., Warfield, S.K., Gholipour, A.: Tversky as a loss function for highly unbalanced image segmentation using 3D fully convolutional deep networks. CoRR abs\/1803.11078 (2018). http:\/\/arxiv.org\/abs\/1803.11078"},{"issue":"1","key":"29_CR17","doi-asserted-by":"publisher","first-page":"226","DOI":"10.3390\/cancers6010226","volume":"6","author":"Maria-del-Mar Inda","year":"2014","unstructured":"Inda, M.d.M., Bonavia, R., Seoane, J., et al.: Glioblastoma multiforme: a look inside its heterogeneous nature. Cancers 6(1), 226\u2013239 (2014)","journal-title":"Cancers"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.632"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Jang, J., et al.: Medical image matching using variable randomized undersampling probability pattern in data acquisition. In: 2014 International Conference on Electronics, Information and Communications (ICEIC), pp. 1\u20132, January 2014. https:\/\/doi.org\/10.1109\/ELINFOCOM.2014.6914453","DOI":"10.1109\/ELINFOCOM.2014.6914453"},{"issue":"11","key":"29_CR20","doi-asserted-by":"publisher","first-page":"e245","DOI":"10.2196\/jmir.2930","volume":"15","author":"Michael Kistler","year":"2013","unstructured":"Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., B\u00fcchler, P.: The virtual skele-ton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11) (2013)","journal-title":"Journal of Medical Internet Research"},{"issue":"4","key":"29_CR21","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s10278-017-9976-3","volume":"30","author":"MD Kohli","year":"2017","unstructured":"Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning\u2014whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30(4), 392\u2013399 (2017)","journal-title":"J. Digit. Imaging"},{"issue":"7553","key":"29_CR22","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"6","key":"29_CR23","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1007\/s10278-014-9705-0","volume":"27","author":"D Mahapatra","year":"2014","unstructured":"Mahapatra, D.: Automatic cardiac segmentation using semantic information from random forests. J. Digit. Imaging 27(6), 794\u2013804 (2014)","journal-title":"J. Digit. Imaging"},{"key":"29_CR24","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","volume":"35","author":"O Maier","year":"2017","unstructured":"Maier, O., et al.: ISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med. Image Anal. 35, 250\u2013269 (2017)","journal-title":"Med. Image Anal."},{"issue":"10","key":"29_CR25","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34(10), 1993\u20132024 (2015)","journal-title":"IEEE transactions on medical imaging"},{"key":"29_CR26","unstructured":"Mirza, M., Osindero, S.: Conditional generative adversarial nets. CoRR abs\/1411.1784 (2014)"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Moeskops, P., Veta, M., Lafarge, M.W., Eppenhof, K.A.J., Pluim, J.P.W.: Adversarial training and dilated convolutions for brain MRI segmentation. CoRR abs\/1707.03195 (2017). http:\/\/arxiv.org\/abs\/1707.03195","DOI":"10.1007\/978-3-319-67558-9_7"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. arXiv preprint arXiv:1810.11654 (2018)","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"29_CR29","unstructured":"Nasr, G.E., Badr, E., Joun, C.: Cross entropy error function in neural networks: forecasting gasoline demand. In: FLAIRS Conference, pp. 381\u2013384 (2002)"},{"issue":"2","key":"29_CR30","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/42.836373","volume":"19","author":"LG Ny\u00fal","year":"2000","unstructured":"Ny\u00fal, L.G., Udupa, J.K., Zhang, X.: New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2), 143\u2013150 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"29_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/3-540-45493-4_20","volume-title":"Computational Intelligence. Theory and Applications","author":"V Palade","year":"2001","unstructured":"Palade, V., Neagu, D.-C., Patton, R.J.: Interpretation of trained neural networks by rule extraction. In: Reusch, B. (ed.) Fuzzy Days 2001. LNCS, vol. 2206, pp. 152\u2013161. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-45493-4_20"},{"issue":"1","key":"29_CR32","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neuroimage.2005.11.044","volume":"31","author":"KM Pohl","year":"2006","unstructured":"Pohl, K.M., Fisher, J., Grimson, W.E.L., Kikinis, R., Wells, W.M.: A Bayesian model for joint segmentation and registration. NeuroImage 31(1), 228\u2013239 (2006)","journal-title":"NeuroImage"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Rezaei, M., Yang, H., Meinel, C.: Instance tumor segmentation using multitask convolutional neural network. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138, July 2018. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489105","DOI":"10.1109\/IJCNN.2018.8489105"},{"key":"29_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-319-75238-9_21","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"M Rezaei","year":"2018","unstructured":"Rezaei, M., et al.: A conditional adversarial network for semantic segmentation of brain tumor. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 241\u2013252. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_21"},{"key":"29_CR35","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1007\/978-3-319-70093-9_85","volume-title":"Neural Information Processing","author":"M Rezaei","year":"2017","unstructured":"Rezaei, M., Yang, H., Meinel, C.: Deep neural network with l2-norm unit for brain lesions detection. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNCS, vol. 10637, pp. 798\u2013807. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70093-9_85"},{"key":"29_CR36","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/978-3-662-56537-7_89","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2018","author":"M Rezaei","year":"2018","unstructured":"Rezaei, M., Yang, H., Meinel, C.: Whole heart and great vessel segmentation with context-aware of generative adversarial networks. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds.) Bildverarbeitung f\u00fcr die Medizin 2018. Informatik aktuell, pp. 353\u2013358. Springer, Heidelberg (2018). https:\/\/doi.org\/10.1007\/978-3-662-56537-7_89"},{"key":"29_CR37","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":"29_CR38","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., et al. (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":"29_CR39","doi-asserted-by":"crossref","unstructured":"Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation. arXiv preprint arXiv:1810.07884 (2018)","DOI":"10.1007\/978-3-030-11726-9_6"},{"key":"29_CR40","doi-asserted-by":"crossref","unstructured":"Xu, J., Schwing, A.G., Urtasun, R.: Tell me what you see and i will show you where it is. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3190\u20133197 (2014)","DOI":"10.1109\/CVPR.2014.408"},{"key":"29_CR41","unstructured":"Xue, Y., Xu, T., Zhang, H., Long, L.R., Huang, X.: Segan: adversarial network with multi-scalel1 loss for medical image segmentation. CoRR abs\/1706.01805 (2017)"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11726-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:45:20Z","timestamp":1710333920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11726-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030117252","9783030117269"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11726-9_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"95","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":"92","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":"97% - 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":"3","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}