{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T17:33:40Z","timestamp":1777656820113,"version":"3.51.4"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T00:00:00Z","timestamp":1496361600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T00:00:00Z","timestamp":1496361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA160045"],"award-info":[{"award-number":["U01CA160045"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["U01CA142555"],"award-info":[{"award-number":["U01CA142555"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2017,8]]},"DOI":"10.1007\/s10278-017-9983-4","type":"journal-article","created":{"date-parts":[[2017,6,2]],"date-time":"2017-06-02T20:36:55Z","timestamp":1496435815000},"page":"449-459","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":942,"title":["Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions"],"prefix":"10.1007","volume":"30","author":[{"given":"Zeynettin","family":"Akkus","sequence":"first","affiliation":[]},{"given":"Alfiia","family":"Galimzianova","sequence":"additional","affiliation":[]},{"given":"Assaf","family":"Hoogi","sequence":"additional","affiliation":[]},{"given":"Daniel L.","family":"Rubin","sequence":"additional","affiliation":[]},{"given":"Bradley J.","family":"Erickson","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,6,2]]},"reference":[{"issue":"7553","key":"9983_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"Y. LeCun, Y. Bengio, and G. Hinton, \u201cDeep learning,\u201d Nature, vol. 521, no. 7553, pp. 436\u2013444, 2015.","journal-title":"Nature"},{"key":"9983_CR2","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.neucom.2016.08.039","volume":"216","author":"D Lin","year":"2016","unstructured":"Lin D, Vasilakos AV, Tang Y, Yao Y: Neural networks for computer-aided diagnosis in medicine: A review. Neurocomputing 216:700\u2013708, 2016","journal-title":"Neurocomputing"},{"key":"9983_CR3","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","volume":"35","author":"T Kooi","year":"2017","unstructured":"Kooi T et al.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35:303\u2013312, 2017","journal-title":"Med. Image Anal."},{"key":"9983_CR4","doi-asserted-by":"publisher","first-page":"24454","DOI":"10.1038\/srep24454","volume":"6","author":"J-Z Cheng","year":"2016","unstructured":"Cheng J-Z et al.: Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6:24454, 2016","journal-title":"Sci. Rep."},{"key":"9983_CR5","doi-asserted-by":"publisher","first-page":"26286","DOI":"10.1038\/srep26286","volume":"6","author":"G Litjens","year":"2016","unstructured":"Litjens G et al.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6:26286, 2016","journal-title":"Sci. Rep."},{"issue":"4","key":"9983_CR6","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"Y. LeCun et al., \u201cBackpropagation applied to handwritten zip code recognition,\u201d Neural Comput., vol. 1, no. 4, pp. 541\u2013551, 1989.","journal-title":"Neural Comput."},{"key":"9983_CR7","doi-asserted-by":"crossref","unstructured":"Deng J, et al.: \u201cImageNet: A large-scale hierarchical image database,\u201d in 2009 I.E. Conference on Computer Vision and Pattern Recognition, 2009.","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"3","key":"9983_CR8","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"O. Russakovsky et al., \u201cImageNet large scale visual recognition challenge,\u201d Int. J. Comput. Vis., vol. 115, no. 3, pp. 211\u2013252, 2015.","journal-title":"Int. J. Comput. Vis."},{"key":"9983_CR9","first-page":"1097","volume-title":"Advances in neural information processing systems 25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ Eds. Advances in neural information processing systems 25. USA: Curran Associates, Inc., 2012, pp. 1097\u20131105"},{"key":"9983_CR10","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J: \u201cDelving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,\u201d in 2015 I.E. International Conference on Computer Vision (ICCV), 2015.","DOI":"10.1109\/ICCV.2015.123"},{"issue":"1","key":"9983_CR11","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.ijrobp.2004.01.026","volume":"59","author":"GP Mazzara","year":"2004","unstructured":"G. P. Mazzara, R. P. Velthuizen, J. L. Pearlman, H. M. Greenberg, and H. Wagner, \u201cBrain tumor target volume determination for radiation treatment planning through automated MRI segmentation,\u201d Int. J. Radiat. Oncol. Biol. Phys., vol. 59, no. 1, pp. 300\u2013312, 2004.","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"7","key":"9983_CR12","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"S. K. Warfield, K. H. Zou, and W. M. Wells, \u201cSimultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation,\u201d IEEE Trans. Med. Imaging, vol. 23, no. 7, pp. 903\u2013921, 2004.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"9983_CR13","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1109\/TMI.2014.2329603","volume":"33","author":"A Akhondi-Asl","year":"2014","unstructured":"A. Akhondi-Asl, L. Hoyte, M. E. Lockhart, and S. K. Warfield, \u201cA logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights,\u201d IEEE Trans. Med. Imaging, vol. 33, no. 10, pp. 1997\u20132009, 2014.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"9983_CR14","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.neuroimage.2008.12.037","volume":"46","author":"A Klein","year":"2009","unstructured":"A. Klein et al., \u201cEvaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration,\u201d Neuroimage, vol. 46, no. 3, pp. 786\u2013802, 2009.","journal-title":"Neuroimage"},{"issue":"3","key":"9983_CR15","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"S. M. Smith, \u201cFast robust automated brain extraction,\u201d Hum. Brain Mapp., vol. 17, no. 3, pp. 143\u2013155, 2002.","journal-title":"Hum. Brain Mapp."},{"issue":"9","key":"9983_CR16","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1109\/TMI.2011.2138152","volume":"30","author":"JE Iglesias","year":"2011","unstructured":"J. E. Iglesias, C.-Y. Liu, P. M. Thompson, and Z. Tu, \u201cRobust brain extraction across datasets and comparison with publicly available methods,\u201d IEEE Trans. Med. Imaging, vol. 30, no. 9, pp. 1617\u20131634, 2011.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"9983_CR17","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","volume":"26","author":"J Ashburner","year":"2005","unstructured":"J. Ashburner and K. J. Friston, \u201cUnified segmentation,\u201d Neuroimage, vol. 26, no. 3, pp. 839\u2013851, 2005.","journal-title":"Neuroimage"},{"issue":"3","key":"9983_CR18","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TMI.2006.891486","volume":"26","author":"U Vovk","year":"2007","unstructured":"U. Vovk, F. Pernus, and B. Likar, \u201cA review of methods for correction of intensity inhomogeneity in MRI,\u201d IEEE Trans. Med. Imaging, vol. 26, no. 3, pp. 405\u2013421, 2007.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"9983_CR19","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1002\/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M","volume":"42","author":"LG Ny\u00fal","year":"1999","unstructured":"L. G. Ny\u00fal and J. K. Udupa, \u201cOn standardizing the MR image intensity scale,\u201d Magn. Reson. Med., vol. 42, no. 6, pp. 1072\u20131081, 1999.","journal-title":"Magn. Reson. Med."},{"issue":"4","key":"9983_CR20","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TMI.2007.906087","volume":"27","author":"P Coupe","year":"2008","unstructured":"P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot, \u201cAn optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images,\u201d IEEE Trans. Med. Imaging, vol. 27, no. 4, pp. 425\u2013441, 2008.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9983_CR21","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","volume":"36","author":"K Kamnitsas","year":"2016","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, 2016","journal-title":"Med. Image Anal."},{"key":"9983_CR22","doi-asserted-by":"crossref","unstructured":"Pereira S, Pinto A, Alves V, Silva CA: \u201cBrain Tumor Segmentation using Convolutional Neural Networks in MRI Images,\u201d IEEE Trans. Med. Imaging, Mar. 2016.","DOI":"10.1109\/TMI.2016.2538465"},{"issue":"Pt 2","key":"9983_CR23","first-page":"649","volume":"16","author":"G Wu","year":"2013","unstructured":"G. Wu, M. Kim, Q. Wang, Y. Gao, S. Liao, and D. Shen, \u201cUnsupervised deep feature learning for deformable registration of MR brain images,\u201d Med. Image Comput. Comput. Assist. Interv., vol. 16, no. Pt 2, pp. 649\u2013656, 2013.","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"9983_CR24","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neuroimage.2016.01.024","volume":"129","author":"J Kleesiek","year":"2016","unstructured":"Kleesiek J et al.: Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. Neuroimage 129:460\u2013469, 2016","journal-title":"Neuroimage"},{"key":"9983_CR25","doi-asserted-by":"crossref","unstructured":"Gondara L: \u201cMedical image denoising using convolutional denoising autoencoders,\u201d arXiv [cs.CV], 2016.","DOI":"10.1109\/ICDMW.2016.0041"},{"key":"9983_CR26","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2016","unstructured":"Havaei M et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35:18\u201331, 2016","journal-title":"Med. Image Anal."},{"key":"9983_CR27","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","volume":"108","author":"W Zhang","year":"2015","unstructured":"Zhang W et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108:214\u2013224, 2015","journal-title":"Neuroimage"},{"issue":"5","key":"9983_CR28","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1109\/TMI.2016.2548501","volume":"35","author":"P Moeskops","year":"2016","unstructured":"P. Moeskops et al., \u201cAutomatic segmentation of MR brain images with a convolutional neural network,\u201d IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1252\u20131261, 2016.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9983_CR29","unstructured":"Akkus Z, et al.: \u201cPredicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning,\u201d arXiv [cs.CV], 2016."},{"key":"9983_CR30","doi-asserted-by":"crossref","unstructured":"Nie D, Dong N, Li W, Yaozong G, Dinggang S: \u201cFully convolutional networks for multi-modality isointense infant brain image segmentation,\u201d in 2016 I.E. 13th International Symposium on Biomedical Imaging (ISBI), 2016.","DOI":"10.1109\/ISBI.2016.7493515"},{"issue":"5","key":"9983_CR31","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1109\/TMI.2016.2528821","volume":"35","author":"T Brosch","year":"2016","unstructured":"T. Brosch et al., \u201cDeep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation,\u201d IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1229\u20131239, 2016.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"9983_CR32","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1109\/TMI.2016.2528129","volume":"35","author":"Q Dou","year":"2016","unstructured":"Q. Dou et al., \u201cAutomatic detection of cerebral Microbleeds from MR images via 3D convolutional neural networks,\u201d IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1182\u20131195, 2016.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9983_CR33","doi-asserted-by":"crossref","unstructured":"Srhoj-Egekher V, Manon JN, Viergever MA, I\u0161gum I: \u201cAutomatic neonatal brain tissue segmentation with MRI,\u201d in Medical Imaging 2013: Image Processing, 2013.","DOI":"10.1117\/12.2006653"},{"issue":"12","key":"9983_CR34","doi-asserted-by":"publisher","first-page":"e81895","DOI":"10.1371\/journal.pone.0081895","volume":"8","author":"P Anbeek","year":"2013","unstructured":"P. Anbeek et al., \u201cAutomatic segmentation of eight tissue classes in neonatal brain MRI,\u201d PLoS One, vol. 8, no. 12, p. e81895, 2013.","journal-title":"PLoS One"},{"issue":"1","key":"9983_CR35","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.neuroimage.2007.05.018","volume":"37","author":"HA Vrooman","year":"2007","unstructured":"H. A. Vrooman et al., \u201cMulti-spectral brain tissue segmentation using automatically trained k-nearest-neighbor classification,\u201d Neuroimage, vol. 37, no. 1, pp. 71\u201381, 2007.","journal-title":"Neuroimage"},{"issue":"9","key":"9983_CR36","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1109\/TMI.2014.2322280","volume":"33","author":"A Makropoulos","year":"2014","unstructured":"A. Makropoulos et al., \u201cAutomatic whole brain MRI segmentation of the developing neonatal brain,\u201d IEEE Trans. Med. Imaging, vol. 33, no. 9, pp. 1818\u20131831, 2014.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9983_CR37","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.neuroimage.2014.12.042","volume":"108","author":"L Wang","year":"2015","unstructured":"Wang L et al.: LINKS: Learning-based multi-source IntegratioN frameworK for segmentation of infant brain images. Neuroimage 108:160\u2013172, 2015","journal-title":"Neuroimage"},{"key":"9983_CR38","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.neuroimage.2015.06.007","volume":"118","author":"P Moeskops","year":"2015","unstructured":"Moeskops P et al.: Automatic segmentation of MR brain images of preterm infants using supervised classification. Neuroimage 118:628\u2013641, Sep. 2015","journal-title":"Neuroimage"},{"key":"9983_CR39","doi-asserted-by":"crossref","unstructured":"Chi\u0163\u0103 SM, Benders M, Moeskops P, Kersbergen KJ, Viergever MA, I\u0161gum I: \u201cAutomatic segmentation of the preterm neonatal brain with MRI using supervised classification,\u201d in Medical Imaging 2013: Image Processing, 2013.","DOI":"10.1117\/12.2006753"},{"key":"9983_CR40","doi-asserted-by":"crossref","unstructured":"A. de Brebisson, M. Giovanni: \u201cDeep neural networks for anatomical brain segmentation,\u201d in 2015 I.E. Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.","DOI":"10.1109\/CVPRW.2015.7301312"},{"key":"9983_CR41","doi-asserted-by":"crossref","unstructured":"Bao S, Siqi B, Chung ACS: \u201cMulti-scale structured CNN with label consistency for brain MR image segmentation,\u201d Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, pp. 1\u20135, 2016.","DOI":"10.1080\/21681163.2016.1182072"},{"issue":"3","key":"9983_CR42","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"D. W. Shattuck et al., \u201cConstruction of a 3D probabilistic atlas of human cortical structures,\u201d Neuroimage, vol. 39, no. 3, pp. 1064\u20131080, 2008.","journal-title":"Neuroimage"},{"issue":"10","key":"9983_CR43","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1109\/TMI.2015.2419072","volume":"34","author":"CH Sudre","year":"2015","unstructured":"C. H. Sudre, M. J. Cardoso, W. H. Bouvy, G. J. Biessels, J. Barnes, and S. Ourselin, \u201cBayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation,\u201d IEEE Trans. Med. Imaging, vol. 34, no. 10, pp. 2079\u20132102, 2015.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"Pt A","key":"9983_CR44","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1016\/j.neuroimage.2015.09.047","volume":"124","author":"A Galimzianova","year":"2016","unstructured":"A. Galimzianova, F. Pernu\u0161, B. Likar, and \u017d. \u0160piclin, \u201cStratified mixture modeling for segmentation of white-matter lesions in brain MR images,\u201d Neuroimage, vol. 124, no. Pt A, pp. 1031\u20131043, 2016.","journal-title":"Neuroimage"},{"issue":"Pt 1","key":"9983_CR45","first-page":"735","volume":"16","author":"N Weiss","year":"2013","unstructured":"N. Weiss, D. Rueckert, and A. Rao, \u201cMultiple sclerosis lesion segmentation using dictionary learning and sparse coding,\u201d Med. Image Comput. Comput. Assist. Interv., vol. 16, no. Pt 1, pp. 735\u2013742, 2013.","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"issue":"6","key":"9983_CR46","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TMI.2014.2382561","volume":"34","author":"Z Karimaghaloo","year":"2015","unstructured":"Z. Karimaghaloo, H. Rivaz, D. L. Arnold, D. L. Collins, and T. Arbel, \u201cTemporal hierarchical adaptive texture CRF for automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI,\u201d IEEE Trans. Med. Imaging, vol. 34, no. 6, pp. 1227\u20131241, 2015.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"9983_CR47","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/TMI.2015.2393853","volume":"34","author":"X Tomas-Fernandez","year":"2015","unstructured":"X. Tomas-Fernandez and S. K. Warfield, \u201cA model of population and subject (MOPS) intensities with application to multiple sclerosis lesion segmentation,\u201d IEEE Trans. Med. Imaging, vol. 34, no. 6, pp. 1349\u20131361, 2015.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"9983_CR48","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1016\/j.neuroimage.2009.09.005","volume":"49","author":"N Shiee","year":"2010","unstructured":"N. Shiee, P.-L. Bazin, A. Ozturk, D. S. Reich, P. A. Calabresi, and D. L. Pham, \u201cA topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions,\u201d Neuroimage, vol. 49, no. 2, pp. 1524\u20131535, 2010.","journal-title":"Neuroimage"},{"issue":"3","key":"9983_CR49","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.media.2004.06.007","volume":"8","author":"M Prastawa","year":"2004","unstructured":"M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, \u201cA brain tumor segmentation framework based on outlier detection,\u201d Med. Image Anal., vol. 8, no. 3, pp. 275\u2013283, 2004.","journal-title":"Med. Image Anal."},{"issue":"13","key":"9983_CR50","doi-asserted-by":"publisher","first-page":"R97","DOI":"10.1088\/0031-9155\/58\/13\/R97","volume":"58","author":"S Bauer","year":"2013","unstructured":"S. Bauer, R. Wiest, L.-P. Nolte, and M. Reyes, \u201cA survey of MRI-based medical image analysis for brain tumor studies,\u201d Phys. Med. Biol., vol. 58, no. 13, pp. R97\u2013129, 2013.","journal-title":"Phys. Med. Biol."},{"issue":"8","key":"9983_CR51","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1007\/s00234-011-0992-6","volume":"54","author":"X Llad\u00f3","year":"2012","unstructured":"X. Llad\u00f3 et al., \u201cAutomated detection of multiple sclerosis lesions in serial brain MRI,\u201d Neuroradiology, vol. 54, no. 8, pp. 787\u2013807, 2012.","journal-title":"Neuroradiology"},{"issue":"1","key":"9983_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2012.09.004","volume":"17","author":"D Garc\u00eda-Lorenzo","year":"2013","unstructured":"D. Garc\u00eda-Lorenzo, S. Francis, S. Narayanan, D. L. Arnold, and D. L. Collins, \u201cReview of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging,\u201d Med. Image Anal., vol. 17, no. 1, pp. 1\u201318, 2013.","journal-title":"Med. Image Anal."},{"key":"9983_CR53","doi-asserted-by":"crossref","unstructured":"Zhao L, Jia K: \u201cDeep Feature Learning with Discrimination Mechanism for Brain Tumor Segmentation and Diagnosis,\u201d in 2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2015.","DOI":"10.1109\/IIH-MSP.2015.41"},{"key":"9983_CR54","doi-asserted-by":"crossref","unstructured":"Dvo\u0159\u00e1k P, Pavel D, Bjoern M: \u201cLocal Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation,\u201d in Lecture Notes in Computer Science pp. 59\u201371, 2016.","DOI":"10.1007\/978-3-319-42016-5_6"},{"issue":"12","key":"9983_CR55","doi-asserted-by":"publisher","first-page":"e0145118","DOI":"10.1371\/journal.pone.0145118","volume":"10","author":"O Maier","year":"2015","unstructured":"O. Maier, C. Schr\u00f6der, N. D. Forkert, T. Martinetz, and H. Handels, \u201cClassifiers for ischemic stroke lesion segmentation: A comparison study,\u201d PLoS One, vol. 10, no. 12, p. e0145118, 2015.","journal-title":"PLoS One"},{"key":"9983_CR56","doi-asserted-by":"crossref","unstructured":"Havaei M, Guizard N, Larochelle H, Jodoin PM: \u201cDeep Learning Trends for Focal Brain Pathology Segmentation in MRI,\u201d in Lecture Notes in Computer Science pp. 125\u2013148, 2016.","DOI":"10.1007\/978-3-319-50478-0_6"},{"issue":"10","key":"9983_CR57","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"B. H. Menze et al., \u201cThe multimodal brain tumor image segmentation benchmark (BRATS),\u201d IEEE Trans. Med. Imaging, vol. 34, no. 10, pp. 1993\u20132024, 2015.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9983_CR58","unstructured":"Cho J, Lee K, Shin E, Choy G, Do S: \u201cHow much data is needed to train a medical image deep learning system to achieve necessary high accuracy?,\u201d arXiv [cs.LG], 2015."},{"key":"9983_CR59","doi-asserted-by":"crossref","unstructured":"Lekadir K, et al.: \u201cA Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound,\u201d IEEE J Biomed Health Inform, 2016.","DOI":"10.1109\/JBHI.2016.2631401"},{"key":"9983_CR60","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T: \u201cFully convolutional networks for semantic segmentation,\u201d in 2015 I.E. Conference on Computer Vision and Pattern Recognition (CVPR), 2015.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"9983_CR61","first-page":"3320","volume-title":"Advances in neural information processing systems 27","author":"J Yosinski","year":"2014","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H: How transferable are features in deep neural networks? In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ Eds. Advances in neural information processing systems 27. USA: Curran Associates, Inc., 2014, pp. 3320\u20133328"},{"issue":"5","key":"9983_CR62","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"H.-C. Shin et al., \u201cDeep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,\u201d IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285\u20131298, 2016.","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9983_CR63","doi-asserted-by":"crossref","unstructured":"van Ginneken B, Setio AAA, Jacobs C, Ciompi F: \u201cOff-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans,\u201d in 2015 I.E. 12th International Symposium on Biomedical Imaging (ISBI), 2015.","DOI":"10.1109\/ISBI.2015.7163869"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-017-9983-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-017-9983-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-017-9983-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T03:28:58Z","timestamp":1659065338000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-017-9983-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,2]]},"references-count":63,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,8]]}},"alternative-id":["9983"],"URL":"https:\/\/doi.org\/10.1007\/s10278-017-9983-4","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,2]]},"assertion":[{"value":"2 June 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}