publication . Article . 2016

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

Maglietta R; Amoroso N; Boccardi M; Bruno S; Chincarini A; Gb, Frisoni; Paolo Inglese; Redolfi A; Tangaro S; Tateo A; ...
Open Access English
  • Published: 01 Jan 2016
The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain ...
free text keywords: Classification, MRI, Segmentation, Supervised learning, ddc:616.89, Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Commercial Application, Artificial Intelligence & Image Processing, 0801 Artificial Intelligence And Image Processing, 0802 Computation Theory And Mathematics, 0906 Electrical And Electronic Engineering
Funded by
  • Funder: Canadian Institutes of Health Research (CIHR)
NIH| Alzheimers Disease Neuroimaging Initiative
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
58 references, page 1 of 4

1.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. Research criteria for the diagnosis of alzheimer’s disease: revising the nincdsadrda criteria. Lancet Neurol. 2007; 6: 734-746 [PubMed] [DOI]

Bruno, S, Cercignani, M, Wheeler-Kingshott, C. Neurodegenerative dementias: from MR physics lab to assessment room. Eur Phys J Plus. 2012; 127: 1-15 [OpenAIRE] [DOI]

Bellotti, R, Pascazio, S. Editorial: advanced physical methods in brain research. European Physical Journal Plus. 2012; 127: 1-2 [OpenAIRE] [DOI]

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. The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimers Dementia. 2012; 8: 61-68 [OpenAIRE] [DOI]

Prestia, A, Boccardi, M, Galluzzi, S, Cavedo, E, Adorni, A, Soricelli, A, Bonetti, M, Geroldi, C, Giannakopoulos, P, Thompson, P, Frisoni, G. Hippocampal and amygdalar volume changes in elderly patients with alzheimer’s disease and schizophrenia. Psychiatry Res. 2011; 192 (2): 77-83 [OpenAIRE] [PubMed] [DOI]

Chincarini, A, Bosco, P, Gemme, G, Morbelli, S, Arnaldi, D, Sensi, F, Solano, I, Amoroso, N, Tangaro, S, Longo, R, Squarcia, S, Nobili, F. Alzheimer’s disease markers from structural MRI and FDG-PET brain images. Eur Phys J Plus. 2012; 127: 1-16 [OpenAIRE] [DOI]

Frisoni, G, Jack, C. Harmonization of magnetic resonance-based manual hippocampal segmentation: a mandatory step for wide clinical use. Alzheimers Dement. 2011; 7 (2): 171-4 [OpenAIRE] [PubMed] [DOI]

Wang, H, Suh, JW, Das, SR, Pluta, J, Craige, C, Yushkevich, PA. Multi-atlas segmentation with joint label fusion. Anal Mach Intell. 2013; 35: 611-623 [OpenAIRE] [DOI]

Cootes, T, Taylor, C, Cooper, D, Graham, J. Active shape models-their training and applications. Comput Vis Image Underst. 1995; 61: 38-59 [OpenAIRE] [DOI]

Morra, J, Tu, Z, Apostolova, L, Green, A, Toga, A, Thompson, P. Comparison of adaboost and support vector machines for detecting alzheimer’s disease through automated hippocampal segmentation. IEEE Trans Med Imaging. 2010; 29: 30-43 [OpenAIRE] [PubMed] [DOI]

Morra, JH, Tu, Z, Apostolova, LG, Green, AE, Avedissian, C, Madsen, SK, Parikshak, N, Hua, X, Toga, AW, Jack, CR, Weiner, MW, Thompson, PM. Validation of a fully automated 3D hippocampal segmentation method using subjects with alzheimer’s disease mild cognitive impairment, and elderly controls. Neuroimage. 2008; 43 (1): 59-68 [OpenAIRE] [PubMed] [DOI]

Balafar, M, Ramli, A, Saripan, M, Mashohor, S. Review of brain MRI image segmentation methods. Artif Intell Rev. 2010; 33: 261-274 [OpenAIRE] [DOI]

Morey, Ra, Petty, CM, Xu, Y, Hayes, JP, Wagner II, HW, Lewis, DV, LaBar, KS, Styner, M, McCarthy, G. A comparison of automated segmentation and manual tarcing for quantifying hippocampal and amygala volumes. Neuroimage. 2009; 45 (3): 855-866 [OpenAIRE] [PubMed] [DOI]

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. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neurotechnique. 2002; 33: 341-355

58 references, page 1 of 4
Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue