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

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92 Chapter 5. Liver Vascular Tree Segmentation for liver vessel separation and segmentation. The second method is a standard approach based on level sets ([94]). Both methods require a liver mask for preprocessing and the segmentation. (a) MIP of the dataset using a liver mask. (b) Segmentation results generated with Selle’s approach [130] showing erroneous portal vein and hepatic vein separation. (c) Segmentation result of the level set based method [94] showing isolated vessel segments and leakage of the portal vein tree into hepatic veins and the tumor. (d) Segmentation result of the proposed method. Figure 5.12: Separation and segmentation of liver vessel trees in a contrast enhanced CT dataset with different methods (dark: portal veins, bright: remaining vessels). The dataset contains a tumor in close proximity to the portal vein tree and hepatic veins that overlap in the image with the portal vein tree due to partial volume effects. Figs. 5.12(b) and (c) show segmentation results of these methods on a typical contrast enhanced liver CT dataset. Both methods utilize primarily gray-value information for segmentation. Consequently, the tumor shown in Fig. 5.12(a) is included in the segmentation
5.4. Discussion 93 results. Selle’s approach to tree separation was able to remove major parts of the hepatic veins from the initial portal vein tree segmentation result. However, some errors still remain in the segmentation result (Fig. 5.12(b)). The result achieved with the algorithm of [94] contains some parts of the hepatic vein and the tumor is connected to the portal vein tree (Fig. 5.12(c)). In comparison, our approach resulted in a correct segmentation shown in Fig. 5.12(d). Our structural analysis step during the grouping an linkage of the tubular structures allows to resolve problems and a valid shape prior is generated such that leakage or undersegmentation is avoided. The ability to consider local disturbances in a more global context contributes to the robustness of our approach and is one of its major advantages. 5.4 Discussion We have evaluated our method in Section 5.3 on several different datasets to assess the ability to: a) correctly obtain/separate different tree structures (e.g., vessel systems), b) accurately determine the surface of tubular tree structures (segmentation accuracy), and c) robustly handle noise and disturbances (e.g., tumors). In this section, we discuss different aspects of the evaluation. Structural correctness: On experiments with phantom and clinical data, we demonstrated our method’s ability to identify tubular objects (Section 5.3.1 and 5.3.2). In all experiments, no false positives were detected, demonstrating the robustness of our method to imaging artifacts and noise. With decreasing contrast and scan resolution, the detection of thin tubular objects becomes increasingly difficult, and at some point, tubes become indistinguishable from the image background. Using phantom datasets, we quantified the effect of contrast and resolution on the detectability of tubular objects. On clinical liver CT datasets, we showed the correlation between contrast and the centerline lengths of the extracted vessel trees (Fig. 5.11). For clinical liver datasets, the radiologist identified only a few missing vessel branches (11 (0.26%) out of 4170). On phantom data, far more missing tubes were identified (Fig. 5.3). In case of the phantom datasets, the location and number of tubular branches was known a priori, because of the available ground truth. Note that most of the unidentified tubes in the phantom datasets can not be visually detected in the image data by humans (Fig. 5.2). Clearly, for clinical data, no such ground truth was available. Overall, our method performed well even in case of poor data quality. We demonstrated the ability of our method to separate and segment multiple interwo-
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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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18 Chapter 2. Extraction of Tubular
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Page 64 and 65: 42 Chapter 2. Extraction of Tubular
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Page 94 and 95: 72 Chapter 4. Tube Segmentation (a)
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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 133 and 134: 7.2. Methods 111 shown in Fig. 7.1.
Page 135 and 136: 7.2. Methods 113 Additionally, the
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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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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.
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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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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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