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

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54 Chapter 3. Grouping and Linkage into Connected Networks 3.4 Experiments In this section, we study the behavior of the previously presented grouping and linkage methods (Section 3.2 and 3.3) with respect to the structural correctness of the tubular tree structures and with respect to the accuracy of the extracted curve skeletons. 3.4.1 Structural Correctness To study properties of the method’s abilities to obtain the correct structure, both methods were applied for the task of airway tree reconstruction and liver vascular tree reconstruction/separation (Figs. 3.3 and 3.4). The tubular structures were obtained from the datasets using the GVF-based TDF with the offset medialness function as presented in Section 2.3.2. Organ masks were utilized to discard tubular structures in the datasets. For the structural tree reconstruction method (Section 3.2), the required root elements were selected manually. Ability to separate multiple interwoven tubular tree structures: Fig. 3.3 show the results on a contrast enhanced liver CT dataset. Fig. 3.3(a) and (b) show the original dataset and the extracted tubular structures respectively. The dataset contains the portal vein tree and the hepatic vein trees. Both of them have a comparable gray value in the datasets, and both appear overlapping in some areas due to partial voluming. The dataset further shows a tumorous region adjacent to one of the vessels. As can be seen from the extracted tubular structures, the TDF was able to suppress the tumorous region, however, also the vessel adjacent to the tumor was not identified. Additionally, the centerlines between the tubular structures break up in some areas (e.g. branching areas). Fig. 3.3(c) and (d) show the results of the image based and the structure based approach, respectively, showing all tubular structures identified as part of the portal vein tree in blue. As can be seen, the image based approach was able to close the gap in proximity of the tumor, but it was not able to distinguish between the two vascular tree structures as both overlap in the image domain. Using the presented structure based approach avoids this problem and allows for a correct reconstruction and separation of the vascular trees. Impact of flow direction information on structural correctness: Contrary to other structure based approaches that perform a grouping of unconnected tubular structures into complete tree structures, our method incorporates flow direction information (Section 3.2). To assess the impact of this additional information on robustness, we re-
3.4. Experiments 55 (a) MIP of dataset. (b) Extracted tubular structures. (c) Image based grouping/linkage. (d) Structure based grouping/linkage. (e) Structure based grouping/linkage without angle information. Figure 3.3: Grouping and linkage of tubular structures applied to a liver CT containing multiple interwoven tubular tree structures. Top row: dataset and identified tubular structures. Bottom row: grouping and linkage result for the two methods showing linkage paths (green), identified tubes connected to the portal vein tree by the grouping (blue), and identified tubes not connected to the portal vein tree (red). moved all part of the structural approach that utilizes flow direction (angle) information. A result of applying this simpler algorithm on this dataset is shown in Fig. 3.3(e), where also misconnections between the vascular trees can be observed resulting in a grouping of major part of the portal vein tree as part of a hepatic vein tree. An evaluation of this simpler structure based approach and the approach as presented in Section 3.2 on 5 clinical liver CT datasets showed, that the number of misconnections can be reduced significantly by utilizing flow direction information. Without using flow direction information 23 misconnections occurred, while when using flow direction information only one misconnection occurred. As a basis for these claims, we used evaluated segmentation results that were verified by a trained radiologist (see Section 5.3.2). Thus, utilizing flow direction information in the structural approach enhances the robustness in case of multiple overlapping
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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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104 Chapter 6. Coronary Artery Tree
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106 Chapter 6. Coronary Artery Tree
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Chapter 7 Airway Tree Segmentation
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7.2. Methods 111 shown in Fig. 7.1.
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7.2. Methods 113 Additionally, the
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7.3. Evaluation and Results 115 Gro
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7.3. Evaluation and Results 117 Tab
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7.4. Discussion 119 Figure 7.5: “
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7.5. Conclusion 121 Comparison to o
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7.5. Conclusion 123 Figure 7.6: Exa
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Chapter 8 Conclusion and Outlook Co
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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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