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

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114 Chapter 7. Airway Tree Segmentation (a) (b) (c) (d) (e) (f) Figure 7.4: Illustration of the processing steps of the airway tree reconstruction approach. (a) Volume rendering of the utilized dataset. (b) Tube detection filter response. (c) Centerlines of initially extracted tubular structures. (d) Initially extracted tubular structures with associated radii/tangent directions. (e) Grouping and linkage step showing the identified tubular structures belonging the airway tree (blue), the discarded tubular structures (red), and the closed gaps (green). (f) Reconstructed airway tree. associated radius and tangent direction are indicated using a cylinder with appropriate orientation and radius. As can be seen, major parts of the airway tree can be extracted with this approach. However, two problems remain that have to be addressed. First, the centerlines of the tubular structures may break up at junctions or in disturbed regions (e.g., motion artifacts). Second, some false positive responses caused by other low density (dark) tube-like structures that are not airways are also present.
7.3. Evaluation and Results 115 Grouping and linkage: For reconstruction of the airway tree the structure base approach as presented in Section 3.2 is utilized that is used to identify the tubular structures that are part of the actual airway tree and to discard all other unrelated tubular structures. The method requires information about the root of the tree and the flow direction inside this root element. Therefore, the trachea is identified automatically by searching for the largest tubular structure, it’s flow direction is determined as pointing towards the center of the volume. The result of the tree reconstruction process is shown in Figs. 7.4(e) and (f). Parameters: Following set of parameters is used to process the datasets. The tube detection is performed on 15 radius steps on a logarithmic scale between radii 0.25 mm and 10 mm with η = 0.7 (the variance term of the boundariness samples in the offset medialness function was omitted for radii ≤ 0.5 mm); t high = 35, t low = 25, and t conf = 150 for the centerline extraction; ρ = 0.5, γ a = 90 ◦ , γ r = 1.3, γ d = 40 mm, and γ c = 0.1 for the tree reconstruction. 7.3 Evaluation and Results Both approaches were applied to 40 clinical datasets (with undisclosed gold standard) which were provided by the “Extraction of Airways from CT 2009 (EXACT09)” database (http://image.diku.dk/exact), whose objective is the quantitative evaluation and comparison of airway tree extraction methods. The focus of the database is on the methods ability to successfully extract the structure of the airway trees, while surface accuracy is not considered. The datasets are split in two groups of 20 training datasets, where the parameters are adapted and 20 testing datasets. For information about how the reference segmentations were obtained and the exact definition of the used performance measures we refer to Lo et al. [88]. For evaluation, binary volume datasets are required that contain a single 6-connected airway structure. While the GVF based method (Section 7.2.1) already provides such a segmentation, the airway tree reconstruction method (Section 7.2.2) only produces a 26-connected airway tree skeleton with corresponding radius information. Thus, to obtain a binary volume dataset for the airway tree reconstruction method, we performed an inverse distance transformation to obtain a rough segmentation and dilated the so obtained reconstruction to assure 6-connectivity. In the next two sections, we present quantitative results achieved with our two pre-
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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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48 Chapter 3. Grouping and Linkage
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Page 149 and 150: 8.1. Conclusion 127 arteries - info
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Page 159 and 160: BIBLIOGRAPHY 137 [21] Choyke, P., Y
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Page 163 and 164: BIBLIOGRAPHY 141 [63] Kitslaar, P.
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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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