{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T10:41:41Z","timestamp":1773484901271,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T00:00:00Z","timestamp":1577923200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T00:00:00Z","timestamp":1577923200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["EB006733, EB008374, AG041721, AG049371, AG042599, AG053867, EB022880"],"award-info":[{"award-number":["EB006733, EB008374, AG041721, AG049371, AG042599, AG053867, EB022880"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602307, 61877039"],"award-info":[{"award-number":["61602307, 61877039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY19F020013"],"award-info":[{"award-number":["LY19F020013"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["AA026762"],"award-info":[{"award-number":["AA026762"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s12021-019-09448-5","type":"journal-article","created":{"date-parts":[[2020,1,2]],"date-time":"2020-01-02T22:05:35Z","timestamp":1578002735000},"page":"319-331","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation"],"prefix":"10.1007","volume":"18","author":[{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5236-686X","authenticated-orcid":false,"given":"Hancan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehsan","family":"Adeli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,2]]},"reference":[{"issue":"3","key":"9448_CR1","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1016\/j.neuroimage.2009.02.018","volume":"46","author":"P Aljabar","year":"2009","unstructured":"Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009). Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage, 46(3), 726\u2013738.","journal-title":"NeuroImage"},{"key":"9448_CR2","doi-asserted-by":"crossref","unstructured":"Artaechevarria, X., Mu\u00f1oz-Barrutia, A., and Ortiz-de-Solorzano, C.(2008). \u201cEfficient classifier generation and weighted voting for atlas-based segmentation: Two small steps faster and closer to the combination oracle,\u201d SPIE Medical Imaging, 69141W\u201369141W-9.","DOI":"10.1117\/12.769401"},{"issue":"8","key":"9448_CR3","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1109\/TMI.2009.2014372","volume":"28","author":"X Artaechevarria","year":"2009","unstructured":"Artaechevarria, X., Munoz-Barrutia, A., & Ortiz-de-Solorzano, C. (2009). Combination strategies in multi-atlas image segmentation: Application to brain MR data. Medical Imaging, IEEE Transactions on, 28(8), 1266\u20131277.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"6","key":"9448_CR4","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.1109\/TMI.2012.2190992","volume":"31","author":"AJ Asman","year":"2012","unstructured":"Asman, A. J., & Landman, B. A. (2012). Formulating spatially varying performance in the statistical fusion framework. Medical Imaging, IEEE Transactions on, 31(6), 1326\u20131336.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"2","key":"9448_CR5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.media.2012.10.002","volume":"17","author":"AJ Asman","year":"2013","unstructured":"Asman, A. J., & Landman, B. A. (2013). Non-local statistical label fusion for multi-atlas segmentation. Medical Image Analysis, 17(2), 194\u2013208.","journal-title":"Medical Image Analysis"},{"issue":"7","key":"9448_CR6","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1016\/j.media.2014.06.005","volume":"18","author":"AJ Asman","year":"2014","unstructured":"Asman, A. J., & Landman, B. A. (2014). Hierarchical performance estimation in the statistical label fusion framework. Medical Image Analysis, 18(7), 1070\u20131081.","journal-title":"Medical Image Analysis"},{"issue":"1","key":"9448_CR7","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26\u201341.","journal-title":"Medical Image Analysis"},{"issue":"7","key":"9448_CR8","doi-asserted-by":"publisher","first-page":"1302","DOI":"10.1109\/TMI.2013.2256922","volume":"32","author":"W Bai","year":"2013","unstructured":"Bai, W., Shi, W., O'Regan, D. P., et al. (2013). A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: Application to cardiac MR images. Medical Imaging, IEEE Transactions on, 32(7), 1302\u20131315.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"1","key":"9448_CR9","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2014.09.005","volume":"19","author":"W Bai","year":"2015","unstructured":"Bai, W., Shi, W., Ledig, C., & Rueckert, D. (2015). Multi-atlas segmentation with augmented features for cardiac MR images. Medical Image Analysis, 19(1), 98\u2013109.","journal-title":"Medical Image Analysis"},{"key":"9448_CR10","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.media.2017.08.008","volume":"42","author":"OM Benkarim","year":"2017","unstructured":"Benkarim, O. M., Piella, G., Ballester, M. A. G., et al. (2017). Discriminative confidence estimation for probabilistic multi-atlas label fusion. Medical Image Analysis, 42, 274\u2013287.","journal-title":"Medical Image Analysis"},{"issue":"2","key":"9448_CR11","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.jalz.2014.12.002","volume":"11","author":"M Boccardi","year":"2015","unstructured":"Boccardi, M., Bocchetta, M., Morency, F. C., Collins, D. L., Nishikawa, M., Ganzola, R., Grothe, M. J., Wolf, D., Redolfi, A., Pievani, M., Antelmi, L., Fellgiebel, A., Matsuda, H., Teipel, S., Duchesne, S., Jack CR Jr, Frisoni, G. B., & EADC-ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Segmentation and for the Alzheimer's Disease Neuroimaging Initiative. (2015). Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimers Dement, 11(2), 175\u2013183.","journal-title":"Alzheimers Dement"},{"key":"9448_CR12","doi-asserted-by":"crossref","unstructured":"Cao, Y., Yuan, Y., Li, X. et al. (2011). \u201cSegmenting images by combining selected atlases on manifold,\u201d International Conference on Medical Image Computing and Computer-Assisted Intervention, 272\u2013279.","DOI":"10.1007\/978-3-642-23626-6_34"},{"key":"9448_CR13","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","volume":"170","author":"H Chen","year":"2018","unstructured":"Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P. A. (2018). VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170, 446\u2013455.","journal-title":"NeuroImage"},{"issue":"8","key":"9448_CR14","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1109\/TMI.2012.2197406","volume":"31","author":"O Commowick","year":"2012","unstructured":"Commowick, O., Akhondi-Asl, A., & Warfield, S. K. (2012). Estimating a reference standard segmentation with spatially varying performance parameters: Local MAP STAPLE. Medical Imaging, IEEE Transactions on, 31(8), 1593\u20131606.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"2","key":"9448_CR15","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1016\/j.neuroimage.2010.09.018","volume":"54","author":"P Coup\u00e9","year":"2011","unstructured":"Coup\u00e9, P., Manj\u00f3n, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011). Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2), 940\u2013954.","journal-title":"NeuroImage"},{"key":"9448_CR16","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neuroimage.2015.11.073","volume":"127","author":"J Doshi","year":"2016","unstructured":"Doshi, J., Erus, G., Ou, Y., Resnick, S. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., Furth, S., Davatzikos, C., & Alzheimer's Neuroimaging Initiative. (2016). MUSE: MUlti-atlas region segmentation utilizing ensembles of registration algorithms and parameters, and locally optimal atlas selection. NeuroImage, 127, 186\u2013195.","journal-title":"NeuroImage"},{"key":"9448_CR17","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., & Heng, P. A. (2017). 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, 41, 40\u201354.","journal-title":"Medical Image Analysis"},{"key":"9448_CR18","unstructured":"A. K. H. Duc, M. Modat, K. K. Leung et al., \u201cManifold learning for atlas selection in multi-atlas-based segmentation of hippocampus,\u201d Medical Imaging 2012: Image Processing, 8314, 83140Z (2012)."},{"key":"9448_CR19","doi-asserted-by":"crossref","unstructured":"Fang, L., Zhang, L., Nie, D. et al. (2017). \u201cBrain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks,\u201d International Workshop on Patch-based Techniques in Medical Imaging, 12\u201319.","DOI":"10.1007\/978-3-319-67434-6_2"},{"key":"9448_CR20","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2017","unstructured":"Gu, J., Wang, Z., Kuen, J., et al. (2017). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354\u2013377.","journal-title":"Pattern Recognition"},{"issue":"5","key":"9448_CR21","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1088\/0266-5611\/20\/5\/018","volume":"20","author":"E Haber","year":"2004","unstructured":"Haber, E., & Modersitzki, J. (2004). Numerical methods for volume preserving image registration. Inverse Problems, 20(5), 1621.","journal-title":"Inverse Problems"},{"issue":"6","key":"9448_CR22","doi-asserted-by":"publisher","first-page":"2674","DOI":"10.1002\/hbm.22359","volume":"35","author":"Y Hao","year":"2014","unstructured":"Hao, Y., Wang, T., Zhang, X., Duan, Y., Yu, C., Jiang, T., Fan, Y., & Alzheimer's Disease Neuroimaging Initiative. (2014). Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation. Human Brain Mapping, 35(6), 2674\u20132697.","journal-title":"Human Brain Mapping"},{"key":"9448_CR23","doi-asserted-by":"crossref","unstructured":"Haom, Y., Liu, J., Duan, Y. et al. (2012). \u201cLocal label learning (L3) for multi-atlas based segmentation,\u201d SPIE Medical Imaging, 83142E-83142E-8.","DOI":"10.1117\/12.911014"},{"key":"9448_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. et al. (2016a). \u201cIdentity mappings in deep residual networks,\u201d European Conference on Computer Vision, 630\u2013645.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"9448_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. et al. (2016b). \u201cDeep residual learning for image recognition,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"9448_CR26","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2006.05.061","volume":"33","author":"RA Heckemann","year":"2006","unstructured":"Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., & Hammers, A. (2006). Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage, 33(1), 115\u2013126.","journal-title":"NeuroImage"},{"key":"9448_CR27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. et al. (2017). \u201cDensely connected convolutional networks,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"1","key":"9448_CR28","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.media.2015.06.012","volume":"24","author":"JE Iglesias","year":"2015","unstructured":"Iglesias, J. E., & Sabuncu, M. R. (2015). Multi-atlas segmentation of biomedical images: A survey. Medical Image Analysis, 24(1), 205\u2013219.","journal-title":"Medical Image Analysis"},{"issue":"4","key":"9448_CR29","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. A., Fox, N. C., et al. (2008). The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685\u2013691.","journal-title":"Journal of Magnetic Resonance Imaging"},{"issue":"4","key":"9448_CR30","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s12021-010-9096-4","volume":"9","author":"K Jafari-Khouzani","year":"2011","unstructured":"Jafari-Khouzani, K., Elisevich, K. V., Patel, S., & Soltanian-Zadeh, H. (2011). Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques. Neuroinformatics, 9(4), 335\u2013346.","journal-title":"Neuroinformatics"},{"key":"9448_CR31","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J. et al. (2014). \u201cCaffe: Convolutional architecture for fast feature embedding,\u201d Proceedings of the 22nd ACM international conference on Multimedia, 675\u2013678.","DOI":"10.1145\/2647868.2654889"},{"issue":"6","key":"9448_CR32","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.media.2013.02.006","volume":"17","author":"M Jorge Cardoso","year":"2013","unstructured":"Jorge Cardoso, M., Leung, K., Modat, M., Keihaninejad, S., Cash, D., Barnes, J., Fox, N. C., Ourselin, S., & Alzheimer\u2019s Disease Neuroimaging Initiative. (2013). STEPS: Similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation. Medical Image Analysis, 17(6), 671\u2013684.","journal-title":"Medical Image Analysis"},{"issue":"9","key":"9448_CR33","doi-asserted-by":"publisher","first-page":"091701","DOI":"10.1118\/1.4816654","volume":"40","author":"TR Langerak","year":"2013","unstructured":"Langerak, T. R., Berendsen, F. F., Van der Heide, U. A., et al. (2013). Multiatlas-based segmentation with preregistration atlas selection. Medical Physics, 40(9), 091701.","journal-title":"Medical Physics"},{"key":"9448_CR34","first-page":"385","volume":"2012","author":"S Liao","year":"2012","unstructured":"Liao, S., Gao, Y., & Shen, D. (2012). Sparse patch based prostate segmentation in CT images. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI, 2012, 385\u2013392.","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI"},{"issue":"2","key":"9448_CR35","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1109\/TMI.2012.2230018","volume":"32","author":"S Liao","year":"2013","unstructured":"Liao, S., Gao, Y., Lian, J., et al. (2013). Sparse patch-based label propagation for accurate prostate localization in CT images. Medical Imaging, IEEE Transactions on, 32(2), 419\u2013434.","journal-title":"Medical Imaging, IEEE Transactions on"},{"key":"9448_CR36","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015). \u201cFully convolutional networks for semantic segmentation,\u201d Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"3","key":"9448_CR37","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1016\/j.neuroimage.2009.10.026","volume":"49","author":"JMP L\u00f6tj\u00f6nen","year":"2010","unstructured":"L\u00f6tj\u00f6nen, J. M. P., Wolz, R., Koikkalainen, J. R., et al. (2010). Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage, 49(3), 2352\u20132365.","journal-title":"NeuroImage"},{"key":"9448_CR38","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016). \u201cV-net: Fully convolutional neural networks for volumetric medical image segmentation,\u201d 3D Vision (3DV), 2016 Fourth International Conference on, 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"issue":"4","key":"9448_CR39","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1016\/j.neuroimage.2003.11.010","volume":"21","author":"T Rohlfing","year":"2004","unstructured":"Rohlfing, T., Brandt, R., Menzel, R., & Maurer CR Jr. (2004). Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage, 21(4), 1428\u20131442.","journal-title":"NeuroImage"},{"key":"9448_CR40","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). \u201cU-net: Convolutional networks for biomedical image segmentation,\u201d International Conference on Medical Image Computing and Computer-Assisted Intervention, 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"10","key":"9448_CR41","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1109\/TMI.2011.2156806","volume":"30","author":"F Rousseau","year":"2011","unstructured":"Rousseau, F., Habas, P. A., & Studholme, C. (2011). A supervised patch-based approach for human brain labeling. Medical Imaging, IEEE Transactions on, 30(10), 1852\u20131862.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"10","key":"9448_CR42","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.1109\/TMI.2010.2050897","volume":"29","author":"MR Sabuncu","year":"2010","unstructured":"Sabuncu, M. R., Yeo, B. T. T., Van Leemput, K., et al. (2010). A generative model for image segmentation based on label fusion. Medical Imaging, IEEE Transactions on, 29(10), 1714\u20131729.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"10","key":"9448_CR43","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1109\/TMI.2014.2327516","volume":"33","author":"G Sanroma","year":"2014","unstructured":"Sanroma, G., Wu, G., Gao, Y., et al. (2014). Learning to rank atlases for multiple-atlas segmentation. Medical Imaging, IEEE Transactions on, 33(10), 1939\u20131953.","journal-title":"Medical Imaging, IEEE Transactions on"},{"key":"9448_CR44","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.imavis.2019.03.006","volume":"88","author":"P Shamsolmoali","year":"2019","unstructured":"Shamsolmoali, P., Zhang, J., & Yang, J. (2019). Image super resolution by dilated dense progressive network. Image and Vision Computing, 88, 9\u201318.","journal-title":"Image and Vision Computing"},{"key":"9448_CR45","doi-asserted-by":"crossref","unstructured":"Wang, H., Suh, J. W., Das, S. et al. (2011). \u201cRegression-based label fusion for multi-atlas segmentation,\u201d Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 1113\u20131120.","DOI":"10.1109\/CVPR.2011.5995382"},{"issue":"3","key":"9448_CR46","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/TPAMI.2012.143","volume":"35","author":"H Wang","year":"2013","unstructured":"Wang, H., Suh, J. W., Das, S. R., et al. (2013). Multi-atlas segmentation with joint label fusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(3), 611\u2013623.","journal-title":"Pattern Analysis and Machine Intelligence, IEEE Transactions on"},{"issue":"7","key":"9448_CR47","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S. K., Zou, K. H., & Wells, W. M. (2004). Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. Medical Imaging, IEEE Transactions on, 23(7), 903\u2013921.","journal-title":"Medical Imaging, IEEE Transactions on"},{"issue":"10","key":"9448_CR48","doi-asserted-by":"publisher","first-page":"4941","DOI":"10.1109\/TIP.2019.2917283","volume":"28","author":"B Xu","year":"2019","unstructured":"Xu, B., Ye, H., Zheng, Y., Wang, H., Luwang, T., & Jiang, Y. G. (2019). Dense dilated network for video action recognition. IEEE Transactions on Image Processing, 28(10), 4941\u20134953.","journal-title":"IEEE Transactions on Image Processing"},{"key":"9448_CR49","unstructured":"Yang, H., Sun, J., Li, H. et al. (2017). \u201cNeural Multi-Atlas Label Fusion: Application to Cardiac MR Images,\u201d arXiv preprint arXiv:1709.09641."},{"key":"9448_CR50","unstructured":"Yu, F., and Koltun, V. (2015). \u201cMulti-scale context aggregation by dilated convolutions,\u201d arXiv preprint arXiv:1511.07122."},{"key":"9448_CR51","doi-asserted-by":"crossref","unstructured":"Yu, L., Yang, X., Chen, H. et al. (2017). \u201cVolumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images,\u201d AAAI, 66\u201372.","DOI":"10.1609\/aaai.v31i1.10510"},{"issue":"12","key":"9448_CR52","doi-asserted-by":"publisher","first-page":"12NT01","DOI":"10.1088\/1361-6560\/aac712","volume":"63","author":"P Zaffino","year":"2018","unstructured":"Zaffino, P., Ciardo, D., Raudaschl, P., et al. (2018). Multi atlas based segmentation: Should we prefer the best atlas group over the group of best atlases? Physics in Medicine & Biology, 63(12), 12NT01.","journal-title":"Physics in Medicine & Biology"},{"key":"9448_CR53","doi-asserted-by":"crossref","unstructured":"Zhu, H., Cheng, H., and Fan, Y. (2015). \u201cRandom local binary pattern based label learning for multi-atlas segmentation,\u201d SPIE Medical Imaging, 94131B-94131B-8.","DOI":"10.1117\/12.2082381"},{"issue":"1","key":"9448_CR54","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s12021-016-9312-y","volume":"15","author":"H Zhu","year":"2017","unstructured":"Zhu, H., Cheng, H., Yang, X., Fan, Y., & Alzheimer\u2019s Disease Neuroimaging Initiative. (2017). Metric learning for multi-atlas based segmentation of Hippocampus. Neuroinformatics, 15(1), 41\u201350.","journal-title":"Neuroinformatics"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-019-09448-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12021-019-09448-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-019-09448-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T19:32:28Z","timestamp":1665343948000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12021-019-09448-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,2]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["9448"],"URL":"https:\/\/doi.org\/10.1007\/s12021-019-09448-5","relation":{},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,2]]},"assertion":[{"value":"2 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}