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

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90 Chapter 5. Liver Vascular Tree Segmentation (a) Cutting plane through the dataset showing the trunk of the hepatic veins. (b) MIP of the dataset. (c) Segmentation result. Figure 5.8: Successful segmentation of poorly contrasted hepatic vessels (arrow). (a) MIP of dataset. (b) Segmentation result. Figure 5.9: Successful segmentation of vessels in close proximity of a bright tumor. hepatic vessel trees of a liver were combined. Note that we excluded results for portal arteries, because they were only visible in 3 of the 15 utilized datasets (imaging protocol). Data quality is primarily related to the noise level, scan-resolution, and contrast between vessels and liver parenchyma. Note that different vascular systems can have a different score for the same dataset. The segmentation quality measure is utilized to evaluate the quality/accuracy of the identified vessel boundaries. As part of this evaluation, we took the non-uniform contrast agent distribution in different generations of the vessel systems into account. Consequently, we assessed the number of visible generations and the segmentation quality related to different vessel generations. In case of “good” data quality the segmentations were always scored as “good” in all generations, while in cases with
5.3. Evaluation and Results 91 (a) Identified vessels; black arrow indicating vessel that was falsely connected to the wrong parent vessel. (b) Segmentation result showing falsely reconstructed vessel trees. (c) The black line indicates the correct vessel structure. Figure 5.10: Wrong vessel connection identified by radiologist. “poor” data quality and only a few visible generations, the segmentation quality tended to be scored as “ok”. This is in particular the case towards the distal parts of the portal vein tree where the contrast vanishes completely. None of the segmentations was scored as “poor”. A plot of the contrast of the main vessel trunk versus the combined centerline length for each dataset is shown in Fig. 5.11. As can be seen, the contrast varies considerably and has a strong influence (correlation) on the resulting centerline length. Centerline length [m] 10 8 6 4 2 portal veins hepatic veins 0 0 50 100 150 Contrast [HU] Figure 5.11: Relation between image contrast and length of the extracted portal veins and hepatic veins of the liver. 5.3.3 Comparison to Other Methods We compare our method with two different vessel segmentation approaches proposed by Selle et al. [130] and Manniesing et al. [94]. The first method [130] was specifically designed
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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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16 Chapter 1. Introduction in the l
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18 Chapter 2. Extraction of Tubular
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Page 62 and 63: 40 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
Page 103 and 104: 5.3. Evaluation and Results 81 5.3.
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Page 120 and 121: 98 Chapter 6. Coronary Artery Tree
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
Page 139 and 140: 7.3. Evaluation and Results 117 Tab
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 147 and 148: Chapter 8 Conclusion and Outlook Co
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
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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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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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