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78 Chapter 5. Liver Vascular Tree Segmentation presented a refined region growing approach with an automatic threshold determination as a stopping criterion with a method for separation of overlapping vessel trees based on the analysis of skeletons after segmentation. The authors did not present a performance analysis, but mentioned the need to manually adapt parameters for each dataset. In addition, they utilized a tool to correct errors in the tree separation. Soler et al. [133, 134] presented a combined approach for the segmentation of blood vessels and tumorous regions inside the liver with an additional postprocessing for the separation of the blood vessels trees based on a skeletonization. Both approaches require liver masks for preprocessing or analysis of the dataset, respectively, and both systems approach the separation of the blood vessels trees based on the analysis of extracted skeletons after the actual segmentation step. However, such approaches are sensitive to segmentation errors, especially to missing parts or leakage where the topology of the extracted centerlines is incorrect. In this chapter, we present an approach that addresses the simultaneous separation and segmentation of different tubular tree structures for the task of liver vasculature segmentation. The method has been evaluated on phantom and clinical datasets, regarding the structural correctness and the surface accuracy of the segmentations. Results are reported, qualitative properties and limits of the method are shown on clinical datasets, and a comparison to results achieved with other methods is provided. 5.2 Method Our 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 three main processing steps to obtain a segmentation and separation of the individual liver vessel trees: a detection and extraction of tubular structures, a tree reconstruction and separation, and a constrained segmentation. An exemplary dataset with intermediate processing results is shown in Fig. 5.1. Extraction of tubular structures: The TDF of Pock et al. [113] as described in Section 2.2.2 was specifically developed for detection of blood vessels in contrast CT datasets and it showed excellent results in our experiments as shown in Section 2.5.2. Therefore, for extraction of tubular structures, the TDF of Pock et al. [113] is utilized in combination with the height-ridge traversal with hysteresis-thresholding for centerline extraction as described in Section 2.4. To discard short spurious responses of the tube detection filter (noise), all centerlines with an accumulated tube likeliness below t conf are discarded.
5.2. Method 79 (a) MIP of the dataset using a liver mask. (b) Final segmentation of all trees. (c) TDF response. (d) Extracted tubular structures. (e) Reconstructed tree structures. (f) Segmented trees. Figure 5.1: Segmentation of liver vasculature trees from a contrast CT dataset with intermediate processing results. The results of the TDF response and the so extracted tubular structures are shown in Fig. 5.1(c) and (d), respectively. The method allows extraction of all tubular objects without false positives due to image noise. However, the centerlines break up at junction areas and the method can not distinguish between parts of the different vessel trees. Grouping and linkage: To separate the different vessel trees and to close gaps between the individual tubular structures, the structure based grouping and linkage method with the verification of the connection path based on gray value information as presented in Section 3.2 is used. The method requires a seed point for each of the vessels trees at the root of the tubular tree structure as well as the flow direction of each tree. To obtain such seed points in case of a liver masks following observation can be used to specify the tree roots fully automatically. The tubular tree structures enter the organ and branch recursively, whereby their radius decreases and they never exit the organ. Therefore, all tubular structures in the dataset that pass through the surface of the provided organ mask are roots of trees. However, in case such an organ mask is not provided, the roots of the
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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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4 Chapter 1. Introduction (a) Volum
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6 Chapter 1. Introduction (a) Inacc
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8 Chapter 1. Introduction features
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10 Chapter 1. Introduction 1.1.2 Di
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12 Chapter 1. Introduction Input vo
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14 Chapter 1. Introduction part: Me
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16 Chapter 1. Introduction in the l
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18 Chapter 2. Extraction of Tubular
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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.
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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
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8.2. Directions for Future Work 129
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132 Chapter A. List of Publications
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BIBLIOGRAPHY 135 Bibliography [1] A
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BIBLIOGRAPHY 137 [21] Choyke, P., Y
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BIBLIOGRAPHY 139 [41] Florin, C., P
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BIBLIOGRAPHY 141 [63] Kitslaar, P.
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BIBLIOGRAPHY 143 [85] Lindeberg, T.
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BIBLIOGRAPHY 145 [108] O’Donnell,
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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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