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

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6 Chapter 1. Introduction (a) Inaccurate surface. (b) Missing tube branch. (c) Unconnected tubes. Figure 1.3: Various segmentations with comparable surface distance/volume error due to different reasons, showing the reference segmentation (green) and different segmentation results (red). [88, 124]. Schaap et al. [124] annotated in 32 coronary datasets the centerlines of 4 arteries, which took three observers all together approximately 300 hours of work. Lo et al. [88] spend about 750 hours to obtain references of airway trees in 20 chest CT datasets using a semi-automated approach that only focused on structural correctness of the segmentation results, but not on surface accuracy aspects. In some application domains such as liver vasculature segmentation/separation or lung vessel segmentation/separation even more tubular structures must be identified and additionally a separation of multiple interwoven tubular tree structures is required, making it practically infeasible to produce reference segmentations in such application domains. 1.1 Related Work Due to the importance of tubular structure segmentations, difficulties related to this task, as well as varying application requirements, many publications dealing with this problem can be found in the literature. Extensive surveys of existing vessel segmentation techniques can be found in the review of Kirbas and Queck [62] as well as in the recent work of Lesage et al. [79] who provide an excellent overview and classification of the techniques. For an overview of methods for extraction of airways from CT datasets we refer to the work of Sluimer et al. [132], while more recent approaches are described in Lo et al. [88]. Because of the large number of methods published, we refrain from highly detailed discussions of all approaches and refer to the above mentioned review papers. Instead, we provide a brief summary of related vessel/airway segmentation techniques following closely the classification and work of Lesage et al. [79]. In the following, we discuss some general aspects of these methods when applied to heavily branched tubular tree structures, and present the concept of vessel mining. Publications more closely related to parts of our
1.1. Related Work 7 work are discussed in appropriate chapters later in this work. 1.1.1 Models, Features, and Extraction Schemes Lesage et al. [79] provide an excellent and up-to-date overview and classification of 3D vessel segmentation techniques that is also valid for airway segmentation techniques. The different methods known from the literature utilize many different image processing frameworks. In [79] the authors distinguish between three main characteristics of vessel segmentation techniques: models, features, and extraction schemes. Models: Models are used to represent prior knowledge about the target vessel structures. In this context, one may distinguish between photometric and geometric properties of the vessels. Methods utilizing pure photometric models make assumptions about the expected luminance [1, 40, 117, 175] of the vessels and/or the background [40, 52, 93, 125, 126] as well as the image noise [52, 148] to distinguish between vessel structures and non-vessel structures. Methods utilizing geometric models make assumptions about a key characteristic of vessels, namely their specific shape. Therefore, the methods may encode information about their elongation, properties of their centerline [15, 42, 72], and/or their cross-section [26, 27, 42, 43, 150]. Photometric and geometric information are often combined into hybrid models, incorporating assumptions about the spatial appearance of vessels such as Gaussian- or bar-like cross section profiles [64, 70, 135, 166], appearance as a ridge in scale space [2, 12, 45], or template-based approaches [44, 48, 83]. The models typically focus solely on regular vessel segments, while models about bifurcations or anomalies can only be found rarely, although exceptions – typically adapted to specific kinds of disease – can be found [1, 42, 83, 159, 162]. As a general note, these models are often embedded implicitly in the features and extraction schemes discussed below. Features: Features are the actual detectors/filters used to evaluate a vascular model on the image data. Therefor the methods utilize information about the basic image intensity, first order, and/or second order image derivatives to match against the expected model, often embedded in a Gaussian scale-space framework [84, 143] to account for tubular structures of varying size. Features used in the literature are isotropic features where no assumption about the directionality of the vessel is made [3, 74, 75, 138], features making use of the local geometry typically based on derivative features [1, 44, 73, 83, 89, 121] or model fitting [48, 72, 148, 166] with few other techniques. Another approach is utilizing
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Page 5 and 6: Abstract The segmentation of tubula
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Page 9: Acknowledgements I am deeply gratef
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56 Chapter 3. Grouping and Linkage
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72 Chapter 4. Tube Segmentation (a)
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Chapter 5 Liver Vascular Tree Segme
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5.2. Method 79 (a) MIP of the datas
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5.3. Evaluation and Results 81 5.3.
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5.3. Evaluation and Results 83 cent
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5.3. Evaluation and Results 85 (a)
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5.4. Discussion 93 results. Selle
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5.5. Conclusion 95 a prior to const
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98 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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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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