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

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18 Chapter 2 Literature Review Depending on the application and imaging modalities used in acquisition, a wide range of segmentation methods has been developed in order to deal with images of different parts of body. With respect to the level of human involvement, these methods can be classified into three categories: • manual segmentation • semi-automatic segmentation • automatic segmentation. Manual segmentation is usually performed by trained experts following certain protocols defining regions of interest (ROIs) on the image. The anatomical knowledge of the expert contributes to the segmentation when the boundaries of the structures are difficult to detect solely based on image features. Although expert- delineated segmentations are regarded as the ground truth or the golden standard, in practice, manual approaches are subjected to both intra- and inter-rater variability induced by the the human factor. Considerable variability may result in low reproducibility of the manual segmentation. In addition, manually tracing the structures of interest by experts is a laborious task, which can be time consuming and expensive. It might take more than one hour per structure for the expert to manually define the boundary of the structure of interest in the volume, which is impractical and inefficient, especially when large data set is involved. Computer softwares are usually used to assist manual tracing and the edition of the boundaries, such as the publicly available MRIcron (Rorden and Brett, 2000), imageJ (Abramoff et al., 2004), 3D Slicer (Pieper et al., 2006) and ITK-SNAP (Yushkevich et al., 2006). Computerized automatic and semi-automatic methods are introduced to reduce the subjectivity in the manual segmentation, and to increase the efficiency and reproducibility. Semi-automatic segmentation still requires manual operations to introduce prior knowledge, provide guidance to the process, or correct the segmentation results. The correction of boundaries sometimes can take as long as manual
Chapter 2 Literature Review 19 Figure 2.1: Samples of labelings from Talairach and Tournoux (1988). From left to right: axial, coronal, sagittal section. segmentation. In fully automatic methods, the segmentation protocol is trans- lated into the algorithms, and its demand of interaction with the user/operator is minimal. Both semi-automatic and fully automatic segmentations introduce bias arising from the setting of algorithms which tends to segment the image with con- sistent systematic error. Automatic methods may also be less robust against the presence of unseen pathologies and artefacts, if these influences are not explicitly compensated by the algorithm. 2.1.1 Hippocampal atlases and segmentation protocols Atlases in medical image analysis are labeled images that are validated to define the boundary or the area of anatomical or functional structures on the image. One atlas defining the brain anatomical regions widely used in neuroimaging studies was published by Talairach and Tournoux (1988), in which a dissected brain was photographed with the labeling of Brodmann’s area (see for example Figure 2.1). Later, Montreal Neurological Institute (MNI) defined a new standard brain by averaging series of brain MR scans of normal controls. Based on 305 linearly registered MR scans, the MNI305 brain was created by averaging the intensity of all the images (Evans et al., 1993; Collins, 1994). The ICBM152 template was created by averaging 152 affinely registered brain scans (Mazziotta et al., 2001). Due to the neuroanatomical variability beyond linear transformation, and the
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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 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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