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

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80 Chapter 5. Liver Vascular Tree Segmentation tubular tree structures have to be selected manually. The results of the reconstructed and separated vessel trees are shown for each of the trees in Fig. 5.1(e). Tube segmentation: To obtain accurate segmentations of the individual blood vessel trees, the graph cut based approach as presented in Section 4.3 is used that utilizes shape priors based on inverse distance transformations of the tubes centerline/radius. This method is slightly adapted to guarantee non-overlapping segmentation results in case of multiple partly overlapping tubular tree structures. The signed distance D surface (x) that is computed based on a fast marching method is obtained for all tubular tree structures simultaneously and D surface (x) determines the assignment of the individual voxels to the different trees. Examplary results of final liver vasculature tree segmentations are shown in Fig. 5.1(f) separately for each tree and in Fig 5.1(b) for all trees together. Parameters: Following set of parameters is used to process the datasets. The tube detection is performed on 9 radius steps on a linear scale between radii 0.5 mm and 8.5 mm with η = 0.7; t high = 5, t low = 3, and t conf = 300 for the centerline extraction; ρ = 0.5, γ a = 90 ◦ , γ r = 1.3, γ d = 3.0, and c min = 0.1 for the tree reconstruction and separation; and σ = 2.0 mm, α = 0.5, d m = 3.0 mm, and d min = 0.5 mm for the segmentation. 5.3 Evaluation and Results Producing reference segmentations for evaluation of our method is practically infeasible for this task due to the reasons discussed in Section 1.0.3. To overcome this problem, our evaluation is based on two parts, each of them evaluating different aspects of our method with respect to structural correctness and surface accuracy. The first part is based on CT scans of a plastic phantom. Using a digital model of the plastic phantom as ground truth, we quantify the methods tube detection performance and segmentation accuracy under varying imaging conditions. The second part of our evaluation is based on clinical contrast liver CT datasets containing multiple interwoven vessel trees. Based on an assessment by a radiologist, we quantify the ability of our method to correctly obtain the structure of the different trees. In addition, the segmentation accuracy is evaluated based on a qualitative scoring scheme. Finally, a qualitative comparison to results achieved with other methods is presented.
5.3. Evaluation and Results 81 5.3.1 Phantom Vessel Tree To evaluate the performance of our approach under the influence of varying noise levels, scan resolutions, contrast situations, and tube diameters, we produced a plastic phantom “vessel tree” with known digital ground truth. The design of the “vessel tree” (Fig. 5.2(a)), that was manufactured with a rapid prototyping machine, is inspired by the branching pattern of the human portal vein tree of the liver and consists of about 600 cylindrical branches with varying orientation and diameter (1 mm to 16 mm). The phantom was scanned with a Siemens Somatom Sensation 64 CT scanner with various resolutions to produce different noise levels, partial volume effects, and image reconstruction artifacts. To generate different contrast situations, different “backgrounds” were used: water and water enriched with contrast agent (Fig. 5.2(c) and (d)). In Table 5.1, estimates for the contrast (gray value difference between homogeneous regions inside the phantom and the background) and the noise level (standard deviation in a homogeneous background region) are summarized. With decreasing resolution, the noise level decreases due to the averaging effect of the larger volume of a single voxel. For some datasets, the noise level is almost as high as the contrast difference. Table 5.1: Measured contrast difference between phantom and background and standard deviation of the noise for different background types and scan resolutions. Resolution Background water water & contrast agent contrast noise contrast noise [HU] [HU] [HU] [HU] 0.59 × 0.59 × 0.5 mm −44.03 40.31 −55.83 39.68 0.59 × 0.59 × 1.5 mm −43.28 17.25 −55.51 19.60 0.59 × 0.59 × 3.0 mm −42.62 13.11 −55.57 14.77 All CT scans of the phantom were segmented with the proposed method after adaption of the parameters on one single dataset (the high resolution dataset with water as background). The parameters are listed at the end of Section 5.2. The surfaces of the resulting segmentations were registered to the ground truth (rigid transformation) before error measures were calculated. The error measures are based on the centerline descriptions of the ground truth and on the binary segmented volumes. To establish a relationship between an arbitrary point x of the segmentation and a centerline −→ point x j i of the known ground truth, x is assigned to the point that minimizes | xx j i | − rj i . This allows a correct assignment in proximity of branch points. For the computation of average errors, tubes with similar diameter were grouped together into discrete diameter
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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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16 Chapter 1. Introduction in the l
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18 Chapter 2. Extraction of Tubular
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Page 133 and 134: 7.2. Methods 111 shown in Fig. 7.1.
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Page 149 and 150: 8.1. Conclusion 127 arteries - info
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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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