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

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124 Chapter 7. Airway Tree Segmentation (a) (b) (c) (d) Figure 7.7: Emphysema lung CT data. (a) Volume rendering of the dataset. (b) Identified tubular structures. Note that some of the “pathological” structures have been detected as tubular structures. (c) Tree reconstruction step showing the identified tubular structures belonging to the airway tree (blue), the discarded tubular structures (red), and the closed gaps (green). (d) Reconstructed airway tree. (a) (b) (c) (d) Figure 7.8: Examples of reconstructed airways with a high “leakage volume”. (a) CASE40: “leakage count”=17; “leakage volume”=3678.0 mm 3 . (b) CASE25: “leakage count”=9; “leakage volume”=1693.0 mm 3 . (c) CASE22: “leakage count”=23; “leakage volume”=1311.2 mm 3 . (d) CASE22 comparison to gold standard: voxels classified as leakage (red), correct voxels (green), missed airways (yellow).
Chapter 8 Conclusion and Outlook Contents 8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 8.2 Directions for Future Work . . . . . . . . . . . . . . . . . . . . . 128 8.1 Conclusion In this work we were dealing with the segmentation of 3D tubular tree structures in medical images. In the introduction, we outlined the general requirements and objectives for different applications in this field, emphasizing the importance of the structural correctness, and reviewed related work on vessel/airway segmentation. Based on that we presented a general approach for segmentation of branched tubular networks (Section 1.2) that puts a strong focus on the structural correctness of the segmentation results. The approach consists of a tube extraction step, a grouping and linkage step, and the actual tube segmentation step. We developed and investigated methods for each of these processing steps, leading to a set of flexible methods that can be utilized in various applications. In particular, in Chapter 2 we developed a novel approach for detection of tubular objects based on the GVF that addresses limitations of these typically used Gaussian scale space based methods and compared it to promising TDFs known from the literature. In Chapter 3 we proposed two methods for grouping and linkage of unconnected tubular structures into tubular tree structures, one enabling an extraction of high quality centerlines in that deviate significantly from the typical tube shape, while the other one allows for a separation of interwoven tubular tree structures as well as handling of various kinds of disturbances. 125
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Graz University of Technology Insti
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Abstract The segmentation of tubula
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Kurzfassung Die Segmentierung tubul
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Acknowledgements I am deeply gratef
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Contents 1 Introduction 1 1.0.1 Req
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CONTENTS xv 8 Conclusion and Outloo
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xviii LIST OF FIGURES 4.2 Shape pri
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List of Tables 3.1 Average centerli
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2 Chapter 1. Introduction (a) Porta
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48 Chapter 3. Grouping and Linkage
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Page 133 and 134: 7.2. Methods 111 shown in Fig. 7.1.
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Page 157 and 158: BIBLIOGRAPHY 135 Bibliography [1] A
Page 159 and 160: BIBLIOGRAPHY 137 [21] Choyke, P., Y
Page 161 and 162: BIBLIOGRAPHY 139 [41] Florin, C., P
Page 163 and 164: BIBLIOGRAPHY 141 [63] Kitslaar, P.
Page 165 and 166: BIBLIOGRAPHY 143 [85] Lindeberg, T.
Page 167 and 168: BIBLIOGRAPHY 145 [108] O’Donnell,
Page 169 and 170: BIBLIOGRAPHY 147 [129] Schmugge, S.
Page 171 and 172: BIBLIOGRAPHY 149 [150] van Bemmel,
Page 173: BIBLIOGRAPHY 151 [171] Yi, J. and R
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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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