Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

International A.D (2013) World Alzheimer Report 2013 Overcoming the stigma of dementia Dubois B, Feldman HH, Jacova C, DeKosky ST, Barberger-Gateau P, Cummings J, Delacourte A, Galasko D, Gauthier S, Jicha G, Meguro K, O’Brien J, Pasquier F, Robert P, Rossor M, Salloway S, Stern Y, Visser PJ, Scheltens P (2007) Research criteria for the diagnosis of alzheimer’s disease: revising the nincdsadrda criteria. Lancet Neurol 6:734–746 Bruno S, Cercignani M, Wheeler-Kingshott C (2012) Neurodegenerative dementias: from MR physics lab to assessment room. Eur Phys J Plus 127:1–15 Bellotti R, Pascazio S (2012) Editorial: advanced physical methods in brain research. European Physical Journal Plus 127:1–2 Weiner M, Veitch D, Aisen P, Beckett L, Cairns N, Green R, Harvey D, Jack C, Jagust W, Liu E, Morris J, Petersen R, Saykin A, Schmidt M, Shaw L, Siuciak J, Soares H, Toga A, Trojanowski J (2012) The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dementia 8:61–68 Prestia A, Boccardi M, Galluzzi S, Cavedo E, Adorni A, Soricelli A, Bonetti M, Geroldi C, Giannakopoulos P, Thompson P, Frisoni G (2011) Hippocampal and amygdalar volume changes in elderly patients with alzheimer’s disease and schizophrenia. Psychiatry Res 192(2):77–83 Chincarini A, Bosco P, Gemme G, Morbelli S, Arnaldi D, Sensi F, Solano I, Amoroso N, Tangaro S, Longo R, Squarcia S, Nobili F (2012) Alzheimer’s disease markers from structural MRI and FDG-PET brain images. Eur Phys J Plus 127:1–16 Frisoni G, Jack C (2011) Harmonization of magnetic resonance-based manual hippocampal segmentation: a mandatory step for wide clinical use. Alzheimers Dement 7(2):171–4 Wang H, Suh JW, Das SR, Pluta J, Craige C, Yushkevich PA (2013) Multi-atlas segmentation with joint label fusion. Anal Mach Intell 35:611–623 Cootes T, Taylor C, Cooper D, Graham J (1995) Active shape models-their training and applications. Comput Vis Image Underst 61:38–59 Morra J, Tu Z, Apostolova L, Green A, Toga A, Thompson P (2010) Comparison of adaboost and support vector machines for detecting alzheimer’s disease through automated hippocampal segmentation. IEEE Trans Med Imaging 29:30–43 Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Hua X, Toga AW, Jack CR Jr, Weiner MW, Thompson PM (2008) Validation of a fully automated 3d hippocampal segmentation method using subjects with alzheimer’s disease mild cognitive impairment, and elderly controls. Neuroimage 43(1):59–68 Balafar M, Ramli A, Saripan M, Mashohor S (2010) Review of brain MRI image segmentation methods. Artif Intell Rev 33:261–274 Morey RA, Petty CM, Xu Y, Hayes JP, Wagner HW II, Lewis DV, LaBar KS, Styner M, McCarthy G (2009) A comparison of automated segmentation and manual tarcing for quantifying hippocampal and amygala volumes. Neuroimage 45(3):855–866 Fischl B, Salat D, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale A (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neurotechnique 33:341–355 Bendib M, Merouani H, Diaba F (2014) Automatic segmentation of brain mri through stationary wavelet transform and random forests. Pattern Anal Appl doi:10.1007/s10044-014-0373-y Patenaude B, Smith S, Kennedy D, Jenkinson M (2011) A bayesian model of shape and appearance for subcortical brain. Neuroimage 56(3):907–922 Ortiz A, Gorriz J, Ramirez J, Salas-Gonzalez D, the Alzheimer’s Disease Neuroimaging Initiative F (2012) Improving mri segmentation with probabilistic ghsom and multiobjective optimization. Neurocomputing 114:118–131 Bron E, Smits M, van der Flier WM et al (2015) Standardized evaluation of methods for computer-aided diagnosis of dementia based on structural MRI: the CSDDementia challenge. Neuroimage (in press) Seiffert C, Khoshgoftaar T, Hulse J, Napolitano A (2010) Rusboost: a hybrid approach to alleviating class imbalance. IEEE Trans Syst Man Cybern 40:185–197 Chawla N, Lazarevic A, Hall L, Bowyer K (2003) Smoteboost: improving prediction of the minority class in boosting. In: 7th European conference on principles and practice of knowledge discovery in database pp 107–119 Talln-Ballesterosa A, Hervs-Martfnezb C, Riquelmea J, Ruiz R (2013) Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems. Neurocomputing 114:107–117 Cui Y, Ma H, Saha T (2014) Improvement of power transformer insulation diagnosis using oil characteristics data preprocessed by smoteboost technique. IEEE Trans Dielectr Electr Insul 21:2363–2373 Govindaraj M, Lavanya S (2013) A combined boosting and sampling approach for imbalanced data classification. Int J Adv Res Data Min Cloud Comput 1:44–50 Freund Y, Schapire R (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139 Breiman L (2001) Random forest. Mach Learn 45:5–32 Tangaro S, Amoroso N, Boccardi M, Bruno S, Chincarini A, Ferraro G, Frisoni G, Maglietta R, Redolfi A, Rei L, Tateo A, Bellotti R (2014) Automated voxel-by-voxel tissue classification for hippocampal segmentation: methods and validation. Phys Med 30:878–887 Maglietta R, Amoroso N, Bruno, S., Chincarini, A., Frisoni, G., Inglese, P., Tangaro, S., Tateo, A., Bellotti, R.: Random forest classification for hippocampal segmentation in 3d mr images. In: 12th international conference on machine learning and applications (2013) 264–267 Chyzhyk D, Dacosta-Aguayo R, Mataro M, Grana M (2015) An active learning approach for stroke lesion segmentation on multimodal mri data. Neurocomputing 150:26–36 Sabuncu MR, Yeo BT, Van Leemput K, Fischl B, Golland P (2010) A generative model for image segmentation based on label fusion. IEEE Trans Med Imaging 29(10):1714–1729 Boccardi M, Bocchetta M, Apostolova L, Barnes J, Bartzokis G, Corbetta G, DeCarli C, DeToledo-Morrell L, Firbank M, Ganzola R, Gerritsen L, Henneman W, Killiany R, Malykhin N, Pasqualetti P, Pruessner J, Redolfi A, Robitaille N, Soininen H, Tolomeo D, Wang L, Watson H, Wolf H, Duvernoy H, Duchesne S, Jack C, Frisoni G, for the EADC-ADNI Working Group on the Harmonized Protocol for Manual Hippocampal Segmentation (2015) Delphi definition of the eadc-adni harmonized protocol for hippocampal segmentation on magnetic resonance. Alzheimer’s and Dementia 11:126–138 Frisoni GB, Jack C, Bocchetta M, Bauere C, Frederiksenf K, Liug Y et al (2015) The eadc-adni harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer’s Dementia 11:111–125 Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Computer vision and pattern recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE computer society conference on. vol 1, IEEE pp I–511 Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621 Tesar L, Shimizu A, Smutek D, Kobatake H, Nawano S (2008) Medical image analysis of 3 D CT images based on extension of haralick texture features. Comput Med Imaging Graph 32:513–520 Tangaro S, Amoroso N, Brescia M, Cavuoti S, Chincarini A, Errico R, Inglese P, Longo G, Maglietta R, Tateo A, Riccio G, Bellotti R (2015) Feature selection based on machine learning in mris for hippocampal segmentation. Comput Math Methods Med 2015:10. doi:10.1155/2015/814104 Focke N, Yogarajah M, Symms M, Gruber O, Paulus W, Duncan J (2012) Automated MR image classification in temporal lobe epilepsy. Neuroimage 59(1):356–362 Lotjonen JMP, Wolz R, Koikkalainen JR, Thurfjell L, Waldemar G, Soininen H, Rueckert D, The Alzheimer's Disease Neuroimaging Initiative (2010) Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49(3):2352–2365 Dale A, Fischl B, Sereno MI (1999) Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9:179–194 Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci USA 97:11050–11055 Fischl B, Liu A, Dale AM (2001) Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Med Imaging 20:70–80 Fischl B, Salat DH, van der Kouwe AJ, Makris N, STgonne F, Quinn BT, Dale AM (2004) Sequence-independent segmentation of magnetic resonance images. Neuroimage 23:S69–S84 (Mathematics in brain imaging) Fischl B, Sereno MI, Dale A (1999) Cortical surface-based analysis: Ii: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9:195–207 Fischl B, Sereno MI, Tootell RB, Dale AM (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8:272–284 Fischl B, van der Kouwe A, Destrieux C, Halgren E, STgonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM (2004) Automatically parcellating the human cerebral cortex. Cerebral Cortex 14:11–22 Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale A, Dickerson B, Fischl B (2006) Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32:180–194 Jovicich J, Czanner S, Greve D, Haley E, van der Kouwe A, Gollub R, Kennedy D, Schmitt F, Brown G, MacFall J, Fischl B, Dale A (2006) Reliability in multi-site structural mri studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30:436–443 Segonne F, Dale AM, Busa E, Glessner M, Salat D, Hahn HK, Fischl B (2004) A hybrid approach to the skull stripping problem in mri. Neuroimage 22:1060–1075 Dale A, Sereno M (1993) Improved localization of cortical activity by combining eeg and meg with mri cortical surface reconstruction: a linear approach. J Cogn Neurosci 5:162–176 Reuter M, Rosas HD, Fischl B (2010) Highly accurate inverse consistent registration: a robust approach. Neuroimage 53:1181–1196 Sled J, Zijdenbos A, Evans A (1998) A nonparametric method for automatic correction of intensity nonuniformity in mri data. IEEE Trans Med Imaging 17:87–97 Segonne F, Pacheco J, Fischl B (2007) Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans Med Imaging 26:518–529 Desikan RS, STgonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest. Neuroimage 31:968–980 Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, van der Kouwe A, Jenkins BG, Dale AM, Fischl B (2002) Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology 58:695–701 Kuperberg GR, Broome M, McGuire PK, David AS, Eddy M, Ozawa F, Goff D, West WC, Williams S, van der Kouwe A, Salat D, Dale A, Fischl B (2003) Regionally localized thinning of the cerebral cortex in Schizophrenia. Archives of General Psychiatry 60:878–888 Salat D, Buckner R, Snyder A, Greve DN, Desikan R, Busa E, Morris J, Dale A, Fischl B (2004) Thinning of the cerebral cortex in aging. Cerebral Cortex 14:721–730 Reuter M, Schmansky NJ, Rosas HD, Fischl B (2012) Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61:1402–1418 Reuter M, Fischl B (2011) Avoiding asymmetry-induced bias in longitudinal image processing. Neuroimage 57:19–21