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

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98 Chapter 6. Coronary Artery Tree Extraction follow a tracking paradigm, iteratively expanding already known coronary artery parts starting from a single seed. To the best of our knowledge, the only two exceptions are the work of Szymszack et al. [138] and Florin et al. [39]. Both identify candidate vessel locations using persistent maxima in single image slices and a normal MIP-raycasting technique, respectively. These candidate vessel locations are merged bottom-up into complete tree structures using a minimum spanning tree approach. The method of Florin et al. [39] requires a heart segmentation to limit the vessel candidate detection to a small region at the hearts surface and to avoid false candidates. The candidate selection approach of Szymszack et al. [138] is not very discriminative such that the method requires additional pruning steps and a selection of the coronary artery tree to remove false positives. In this chapter, we present an automated approach for the extraction of coronary artery trees from cardiac CTA datasets obtaining curve skeletons for the two largest coronary trees. The approach has been evaluated on a publicly available database. Results as well as a comparison to the results achieved with other methods are presented and discussed. 6.2 Method The method follows our general approach for segmentation of branched tubular networks as outlined in Section 1.2 to achieve a high robustness against disturbances. It combines tree main processing steps for extraction of the coronary artery trees centerlines: an extraction of all tubular structures, a grouping and linkage of the tubular structures, and discarding centerlines not belonging to the coronary arteries based on prior knowledge. The segmentation step of our general approach is skipped as accurate segmentations are not required. An exemplary dataset with intermediate processing results is shown in Fig. 6.1. Extraction of tubular structures: For identification of tubular structures the GVF based TDF with the central medialness function as presented in Section 2.3.1 is utilized. The required initial vector field F n is obtained from an edge potential image to be able to handle varying background conditions (e.g. calcifications) after median filtering of the original dataset (filter size 5 × 5 × 5 voxels). Applying this approach for detection of coronary arteries on heart CTA datasets results in the response shown in Fig. 6.2(c), whereas applying Frangi’s vesselness measure to the original vector field F n results in the response shown in Fig. 6.2(b). The GVF based approach performs well in identifying major parts of the coronary arteries independent of their size and produces clean responses
6.2. Method 99 (a) (b) (c) (d) (e) Figure 6.1: Centerline extraction and vascular tree reconstruction. (a) MIP of the original dataset (b) TDF response. (c) TDF response after suppression of responses inside the lung. (d) Extracted centerlines. (e) Identified coronary artery trees after grouping and linkage into tree structures.
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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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6 Chapter 1. Introduction (a) Inacc
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8 Chapter 1. Introduction features
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12 Chapter 1. Introduction Input vo
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18 Chapter 2. Extraction of Tubular
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Page 99 and 100: Chapter 5 Liver Vascular Tree Segme
Page 101 and 102: 5.2. Method 79 (a) MIP of the datas
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Page 131 and 132: Chapter 7 Airway Tree Segmentation
Page 133 and 134: 7.2. Methods 111 shown in Fig. 7.1.
Page 135 and 136: 7.2. Methods 113 Additionally, the
Page 137 and 138: 7.3. Evaluation and Results 115 Gro
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Page 141 and 142: 7.4. Discussion 119 Figure 7.5: “
Page 143 and 144: 7.5. Conclusion 121 Comparison to o
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Page 149 and 150: 8.1. Conclusion 127 arteries - info
Page 151: 8.2. Directions for Future Work 129
Page 154 and 155: 132 Chapter A. List of Publications
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.
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BIBLIOGRAPHY 149 [150] van Bemmel,
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BIBLIOGRAPHY 151 [171] Yi, J. and R
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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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