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

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52 Chapter 3 Hippocampal segmentation using multiple atlases Algorithm 2 Supervised construction of atlas set 1: Initialize the atlas database with 18 segmented images in IBSR 2: while size of atlas database below a predetermined threshold do 3: Using multi-atlas segmentation propagation method, segment the image dataset with the current atlas database 4: Visually inspect the segmentation results 5: Well segmented images with high consistency between the image and labeling over structures of interest are qualified and added to the atlas database 6: end while coefficient (DSC) score between the segmentation and the ground truth reaches the highest value when approximately 10 image similarity ranked atlases are fused. Considering the fact that there 8 subjects in the IBSR set are under 18 years old, and to avoid tie votes, 9 atlases were selected in experiment for classifier fusion. The selection was based on image similarity measured by NMI between the unlabeled target image and the registered atlases. The segmentation results were visually inspected, with attention paid especially to the lateral ventricle and deep gray matter structures, such as hippocampus, thalamus, caudate, and putamen. Segmentations qualified in terms of their visual consistency between the image and the corresponding segmentation were added to the atlas database. As the size of atlas database grows, this step can be repeated so that more segmented MR images may be added to the atlas database, enhancing its capability in the multi-atlas based approach. In this study, the atlas set was initialized with the IBSR data, in which anatomical structures are manually delineated by experts (Makris et al., 2004). The age of subjects in IBSR dataset ranges from juvenile to 71, including 4 juvenile subjects and another 4 under 18 years old. In the age-based atlas selection (Aljabar et al., 2007), selecting atlases of subjects with similar age to the query provides a good estimation. The demographic information of atlases fused is relevant to the performance of multi-atlas based segmentation. Atlases of younger subjects in IBSR are likely to fail when being propagated to the brain images of a subject in elderly population. By adding to the atlas set well segmented brain images of subjects from elderly population, it may improve the performance in segmenting the images of elderly subjects.
Chapter 3 Hippocampal segmentation using multiple atlases 53 . . . . . . . . . IBSR atlases N = 18 Atlas Set .Add to the atlas set .Population of interest N = 210 . . . . Affine + NRR Registration . . . .Image Set . Fusing 9 atlases by voting . Visual inspection and selection . . . . .· · · .· · · . . . Selected population specifice atlases N = 16, 24, · · · . . . Segmentation Results Figure 3.2: Diagram demonstrating the process building a set of population specific atlases. 3.2.2 Evaluation of atlas set In the multi-atlas based segmentation method, the errors in the labeling of atlases may be propagated to the segmentation results. It is therefore necessary to evaluate the quality of atlas produced by the proposed method. Due to the lack of delineation of anatomical structures by experts, it is impossible to assess the accuracy of segmentation of these atlases quantitatively against the manual segmentation as ground truth. Measurements of segmentation’s overlap with ground
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UNIVERSITE DE BOURGOGNE U.F.R. SCIE
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Résumé L’objectif de cette thè
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Abstract The aim of this thesis is
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TABLE DES MATIÈRES Acknowledgement
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LISTE DES FIGURES 1.1 Cortical atro
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Chapter 6 Conclusions 139 Given 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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BIBLIOGRAPHY 185 Woolrich, M. W., J
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