Segmentation of 3D Tubular Tree Structures in Medical Images ...

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List of Figures 1.1 Examples of 3D tubular structures in medical images. . . . . . . . . . . . . 2 1.2 Representations of tubular tree structures. . . . . . . . . . . . . . . . . . . . 4 1.3 Structure vs. accuracy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 General concept for segmentation of branched tubular networks. . . . . . . 12 2.1 Tubular structures and derived gradient vectors in Gaussian scale space. . . 20 2.2 Cross-section plane spanned by eigenvectors v 1 and v 2 of the Hessian matrix. 22 2.3 Scale space of a CT dataset showing an aorta. . . . . . . . . . . . . . . . . . 24 2.4 Vector fields derived from GVF and from Gaussian scale space . . . . . . . 25 2.5 Initializing the GVF differently for different backgrounds conditions. . . . . 29 2.6 TDF responses for varying tube configurations. . . . . . . . . . . . . . . . . 33 2.7 TDF responses for varying cross section profiles. . . . . . . . . . . . . . . . 34 2.8 Influence of η with the TDF of Pock . . . . . . . . . . . . . . . . . . . . . . 35 2.9 Tubular structure with vanishing contrast and responses of different TDF. . 37 2.10 TDF responses for varying noise levels. . . . . . . . . . . . . . . . . . . . . . 43 2.11 TDF responses on CT angiography image of the brain. . . . . . . . . . . . . 44 2.12 TDF responses on contrast CT dataset of the liver showing tumor. . . . . . 45 2.13 TDF responses on CT showing aorta in proximity of the spine. . . . . . . . 46 3.1 Information used for calculation of the confidence function for structural tree reconstruction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.2 Illustration of the image based grouping and linkage on 2D cross section profiles of some 3D structures. . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3 Grouping and linkage of tubes of a liver. . . . . . . . . . . . . . . . . . . . . 55 3.4 Grouping and linkage of tubes from a diseased airway tree. . . . . . . . . . 57 3.5 Curve skeletons of an airway tree. . . . . . . . . . . . . . . . . . . . . . . . . 58 3.6 Curve skeletons of a diseased aorta. . . . . . . . . . . . . . . . . . . . . . . . 59 3.7 Curve skeletons of an airway tree. . . . . . . . . . . . . . . . . . . . . . . . . 60 3.8 Curve skeletons of a diseased aorta. . . . . . . . . . . . . . . . . . . . . . . . 61 4.1 Illustration of properties of the GVF using a 2D cross section of a 3D branching tubular structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 xvii
Page 1: Graz University of Technology Insti
Page 5 and 6: Abstract The segmentation of tubula
Page 7 and 8: Kurzfassung Die Segmentierung tubul
Page 9: Acknowledgements I am deeply gratef
Page 13 and 14: Contents 1 Introduction 1 1.0.1 Req
Page 15: CONTENTS xv 8 Conclusion and Outloo
Page 19: LIST OF FIGURES xix 7.7 Emphysema l
Page 23 and 24: Chapter 1 Introduction Contents 1.1
Page 25 and 26: 3 1.0.1 Requirements and Problems T
Page 27 and 28: 5 Graph-based representations: On a
Page 29 and 30: 1.1. Related Work 7 work are discus
Page 31 and 32: 1.1. Related Work 9 by iterative pr
Page 33 and 34: 1.2. A General Approach for Segment
Page 35 and 36: 1.3. Overview and Contributions of
Page 37 and 38: 1.3. Overview and Contributions of
Page 39 and 40: Chapter 2 Extraction of Tubular Str
Page 41 and 42: 2.2. Tube Detection Filters in Gaus
Page 43 and 44: 2.2. Tube Detection Filters in Gaus
Page 45 and 46: 2.3. Tube Detection using Gradient
Page 53 and 54: 2.5. Experiments 31 height-ridge tr
Page 55 and 56: 2.5. Experiments 33 (a) Datasets fr
Page 57 and 58: 2.5. Experiments 35 However, the ap
Page 59 and 60: 2.5. Experiments 37 (a) 3d volume r
Page 61 and 62: 2.6. Discussion and Conclusion 39 e
Page 63 and 64: 2.6. Discussion and Conclusion 41 a
Page 65 and 66: 2.6. Discussion and Conclusion 43 (
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2.6. Discussion and Conclusion 45 (
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Chapter 3 Grouping and Linkage into
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3.2. Structure Based Approach 49 sk
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3.3. Image Based Approach 51 Figure
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3.3. Image Based Approach 53 can be
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3.4. Experiments 55 (a) MIP of data
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3.4. Experiments 57 (a) (b) (c) (d)
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3.4. Experiments 59 (a) Volume rend
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3.4. Experiments 61 junction with t
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3.5. Dicussion and Conclusion 63 3.
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Chapter 4 Tube Segmentation Content
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4.2. Inverse Gradient Vector Flow T
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4.3. Graph Cut Based Tree Segmentat
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4.4. Experiments 71 representations
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4.4. Experiments 73 (a) Original da
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4.5. Discussion and Conclusion 75 (
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78 Chapter 5. Liver Vascular Tree S
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Chapter 6 Coronary Artery Tree Extr
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6.2. Method 99 (a) (b) (c) (d) (e)
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6.2. Method 101 (a) (b) (c) (d) Fig
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6.3. Evaluation and Results 103 - b
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6.3. Evaluation and Results 105 Tab
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6.5. Conclusion 107 The use of the
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110 Chapter 7. Airway Tree Segmenta
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126 Chapter 8. Conclusion and Outlo
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128 Chapter 8. Conclusion and Outlo
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Appendix A List of Publications In
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Appendix B List of Acronyms a.k.a.
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136 [11] Bennink, H. E., van Assen,
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138 [32] Dou, X., Wu, X., Wahle, A.
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140 [52] Hassouna, M., Farag, A., H
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142 [73] Law, M. and Chung, A. (200
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144 [96] McInerney, T. and Terzopou
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146 [119] Reinbacher, C., Pock, T.,
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148 [140] Tankyevych, O., Talbot, H
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150 [160] Wiemker, R., Bülow, T.,
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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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