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

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12 Chapter 1. Introduction Input volume dataset visualized using Maximum Intensity Projection and an organ mask Extraction of Tubular Structures Structural representation of individual tubular objects Grouping and Linkage into Connected Networks Structural representation of whole branched tubular networks Tube Segmentation Segmentation of branched tubular networks Resulting segmentations of all tubular networks Figure 1.4: General concept for segmentation of branched tubular networks. a branched tubular network are interconnected and from a biological perspective they have to be supplied somehow. Utilizing this fact, the structures of the whole branched tubular networks is recovered also in the disturbed regions. The resulting skeleton-based representations describe the complete structure of the branched tubular structures, but not their surface. 3. Tube segmentation: Segment the branched tubular networks utilizing their already know structures as prior knowledge. During this step the surfaces of the branched tubular networks are obtained and accurately delineated from the background. With this approach we seek for a higher robustness to shortcoming of the tube model or disturbances compared to conventional extraction schemes as discussed in
1.3. Overview and Contributions of the Work 13 Section 1.1.2. “Practically all currently used vessel [tubular tree structure] segmentation methods are based on a growth process emanating from an automatically- or user-defined start point” [47], which results in the problem that in case of larger deviations from the tube model or disturbances major parts of the tree structure may remain unidentified (Section 1.1.2). Contrary to that, with our approach initially tubular structures are identified in a bottom-up fashion such that also in cases where the assumptions of the tube model do not hold still major parts of the tree structure are identified. This information can be utilized in the following processing steps. Additionally, we incorporate the prior knowledge that the individual tubular structures of a branched tubular network are interconnected. This combination allows resolving cases, where typical assumptions of a vessel/airway model may be inadequate, such that the structure of the tubular tree structures can be obtained correctly and later on used for a more accurate segmentation. The outlined approach is of general applicability. It has a strong focus on the structural correctness of the segmentation results, which is of importance for most applications dealing with tubular tree structure. In addition, handling of different kinds of disturbances is considered directly as part the approach. However, it is only an approach and not a specific method. It does not specify how to detect tubular structures, how to group and link them together, or how to utilize the so obtained information for the actual segmentation. And indeed, this may vary from application to application depending on its requirements. Also, depending of the application’s requirements, only parts of this approach may be necessary, e.g. in case only the structural information is of importance but not accurate segmentations, step three – the tube segmentation – can be skipped. But all typical requirements and representations needed in different applications are covered with this approach. 1.3 Overview and Contributions of the Work In this work, we develop and evaluate methods for the segmentation of 3D tubular tree structures in medical images. The goal is to address the outlined requirements and problems found in different clinical applications and to provide suitable solutions. Thereby, we follow the proposed general concept for segmentation of tubular tree structures as outlined in the last section. We present several original contributions that resulted from the research on this topic and that have also been presented in several publications [4–9, 105, 119, 124]. A list of these publications can also be found in Appendix A. The work is roughly organized in two parts, a general methods part and an applications
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Page 5 and 6: Abstract The segmentation of tubula
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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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