Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

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Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, positive): “, 1997), and by using an hidden Markov random field model (Cuadra et al., 2005) as described previously (Gaser, 2009).”
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data (2011, background): “Cuadra et al. 2005; Zaidi et al. 2006; Klauschen et al. 2009 ;d e Boer et al.2010).”
Template-O-Matic: A toolbox for creating customized pediatric templates (2008, methodology): “A Hidden Markov Random Field (HMRF; Cuadra et al., 2005) approach with a small prior probability weighting of 0.15 was used to enforce a more consistent labeling of voxels.”
Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis (2006, background): “As recently as 2005, a comparative study of brain segmentation methods used expert manual segmentation of just two slices from just one normal MR brain study as a ground truth [36].”
Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters (2010, positive): “The segmentation procedure was further extended by accounting for partial volume effects (Tohka et al., 2004), by applying adaptive maximum a posteriori estimations (Rajapakse et al., 1997), and by applying hidden Markov random ﬁ eld model (Cuadra et al., 2005) as described by Gaser (2009).”