Docteur de l'université Automatic Segmentation and Shape Analysis ...

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24 Chapter 2 Literature Review amygdala structures. It is initialized by the manual definition of a bounding box ROI and the seeds placed by the operator in each of the structures. The region growing is guided by the landmark and boundary detection based on extensive use of anatomical priors and image features. The deformation of the region is regularized by Markov random field (MRF), solved using iterative conditional modes algorithm (Besag, 1993). With the automatic definition of the seed point, the fast marching for automated segmentation of the hippocampus (FMASH) by Bishop et al. (2011) propagates the region along the path with smallest resistance defined by a potential function of image intensity using the 3D fast marching method (Sethian, 1996; Deschamps and Cohen, 2000). 2.1.2.2 Shape and appearance based methods Active shape models (ASM, Cootes et al., 1995) are used in medical image segmentation by fitting a parametric shape model to automatically detected image features or manually defined landmarks (Shen et al., 2002). Using shape information, the elastic deformation of the model to match the intensity profile can be restricted to a prior shape subspace learned from the training set (Kelemen et al., 1999). Knowledge of relative position and distance between anatomical structures, and texture descriptors have also been added to the ASM segmentation of hippocampus (Pitiot et al., 2004). A shape-intensity joint prior model for both hippocampus and amygdala (Yang and Duncan, 2004) has been developed with neighbor constraints and the level set formulation of shape (Yang et al., 2004). The active appearance model (AAM, Cootes et al., 2001) is a generative model that accounts for the image intensity and texture, i.e. the ‘appearance,’ in addition to the shape structure of the landmarks. Using the image intensity profile along the normal of the structure boundary, the profile appearance model (Babalola et al., 2007, 2008, 2009) produces the segmentation by matching the model to the image minimizing the square of residual differences. A Bayesian approach for AAM method models the conditional distribution of intensity given shape, and the segmentation is obtained by the MAP estimation of the shape given image
Chapter 2 Literature Review 25 intensities (Patenaude et al., 2011; the implementation is available from FMRIB Software Library, Smith et al., 2004; Woolrich et al., 2009) The state-of-the-art segmentation method publicly available in the FreeSurfer package (Dale et al., 1999; Fischl et al., 1999) labels 37 areas for the whole brain, including 18 subcortical structures. Based on the nonstationary anisotropic MRF modeling of the segmentation, it uses the spatial information from a template in the Talairach space, and a nonlinear transform to account for the inter-subject anatomical variability (Fischl et al., 2002). Forward models of the imaging process are embedded in the segmentation method to achieve the sequence independence (Fischl et al., 2004). 2.1.2.3 Atlas based methods Atlas based segmentation methods are widely employed in medical image analysis. Using manually delineated atlas as gold standard, this method transform the label map of the atlas to the query image by the registration algorithms from the atlas to the query. Registration methods such as elastic (Miller et al., 1993; Iosifescu et al., 1997; Christensen and Johnson, 2001), fluid (Haller et al., 1996, 1997; Hogan et al., 2000; Crum et al., 2001), and optical flow based registration (Baillard et al., 2001; Dawant et al., 1999; Hartmann et al., 1999) have been involved in matching the image with the atlas. In ANIMAL framework (Collins et al., 1995; Collins and Evans, 1997), the subject image is used as the source, and the atlas as the target in the non-rigid registration, and the mapping of the labels is achieved by the inverse transformation recovered by ANIMAL. Based on an initial FreeSurfer labeling, the method by Khan et al. (2008) uses the Large Deformation Diffeomor- phic Metric Mapping (LDDMM, Beg et al., 2005) to search for optimal nonlinear transform between the image and the atlas. Appearance model based methods have been proposed to guide the deformation (Duchesne et al., 2002; Klemencic et al., 2004). Besides the atlas of segmented individual, probabilistic atlases are also used in segmentation, providing the spatial priors such as in the Bayesian
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144 LIST OF PUBLICATIONS using dive
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146 BIBLIOGRAPHY Alzheimer, A. (191
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148 BIBLIOGRAPHY Baillard, C., Hell
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150 BIBLIOGRAPHY of post mortem mag
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152 BIBLIOGRAPHY B. (2008). Three-d
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154 BIBLIOGRAPHY Cootes, T. F., Tay
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156 BIBLIOGRAPHY head in MR images
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158 BIBLIOGRAPHY Evans, A. C., Coll
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BIBLIOGRAPHY 161 Ghanei, A., Soltan
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BIBLIOGRAPHY 163 Heckemann, R. A.,
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BIBLIOGRAPHY 165 R. C. (2003). MRI
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BIBLIOGRAPHY 167 Konrad, C., Ukas,
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BIBLIOGRAPHY 169 Mahony, R. and Man
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BIBLIOGRAPHY 171 disease, mild cogn
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BIBLIOGRAPHY 173 Petersen, R. C., S
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BIBLIOGRAPHY 175 Rorden, C. and Bre
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BIBLIOGRAPHY 177 Shen, K., Bourgeat
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BIBLIOGRAPHY 179 Barillot, C., Hayn
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BIBLIOGRAPHY 181 Tzourio-Mazoyer, N
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BIBLIOGRAPHY 183 Vos, F. M., de Bru
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BIBLIOGRAPHY 185 Woolrich, M. W., J
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