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

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128 Chapter 8. Conclusion and Outlook the seminal ideas of Beichel et al. [10, 113], only the works of Szymszack et al. [138], Florin et al. [39], and Graham et al. [50] presented applications using similar ideas. However, none of these methods has been evaluated on larger sets of clinical datasets or compared to other methods. As seen from the applications summarized above, the objectives and problems that have to be addressed in such application areas for segmentation of tubular tree structures are very diverse and challenging. But with the proposed general approach for segmentation of branched tubular networks (Section 1.2) and the set of developed methods most of the problems occurring in such kinds of datasets can be addressed and the developed methods proofed to be suitable for different application domains. 8.2 Directions for Future Work The general framework and the methods we have developed in this thesis provide a flexible basis for future work in the area of vessel/airway segmentation. It can be adapted to other applications or requirements by replacing or improving individual parts (of our three-step approach). Besides the application and evaluation of the already developed methods in other application areas, we see primarily two further directions for possible future work: Improvement of the robustness of the tube extraction approach: The extraction of tubular structures is the first and most critical part of the overall approach. For larger tubular structures, the presented methods perform well and the developed methods also proofed sufficient for identification of thinner low-contrast vessels in liver and coronary CT datasets (Sections 5 and 6). However, in case of airways still about 1/3 of the airways remain undetected (Section 7). The reasons for this behavior are the complex background in lung CT datasets and the fact that even smaller local disturbances can lead to problems in combination with the used height-ridge traversal procedure and the hysteresis-thresholding (Section 2.4). One may consider to enhance the robustness of the tube extraction method for smaller airways either by appropriate pre-processing of the datasets, post-processing TDF results, or by incorporating additional airway-specific prior knowledge into the airway detection. Therefor, pre-processing techniques for noise suppression specifically developed for tubular structures such as “vessel enhancing diffusion” [95] or “flux-based anisotropic diffusion” [66] could be used. Possible TDF result post-processing methods could combine local evidence about candidate airways by taking their elongated structure into account. This could be based on “improved structure ten-
8.2. Directions for Future Work 129 sors” [65], “tensor voting” [139], “vessel enhancing diffusion” [95], or similar techniques. The last point is that airways can be characterized locally not only as a dark structure surrounded by brighter tissue as assumed by our methods in Section 7, but they are surrounded by an airway wall of a certain thickness. Incorporating properties of this airway wall would enhance the selective of the airway extraction. Visualization and user interaction: Although a fully automated segmentation of the tubular tree structures without the need for any user interaction is desirable, there may still remain cases in clinical practice where fully automated methods may fail. Therefore, to be clinically applicable, a system also has to be able to cope with such cases in a semiautomated way. Our developed general approach performs a bottom-up identification of all tube-like structures in a given volume of interest, such that the overall system is aware of potential problems (e.g. identified but unconnected sub-trees). Combined with an appropriate 3D visualization this allows giving a user feedback about potential errors. The user could then easily verify such situations and in case of errors manual correction could be used as such cases occur only very rarely. This behavior of our general approach that allows handling of such errors (unconnected sub-trees) is in contrast to other conventional tubular tree structure segmentation approaches that simply discard the remaining tree parts, which is a severe drawback of conventional tube extraction schemes.
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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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8 Chapter 1. Introduction features
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48 Chapter 3. Grouping and Linkage
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Page 101 and 102: 5.2. Method 79 (a) MIP of the datas
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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
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Page 149: 8.1. Conclusion 127 arteries - info
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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.
Page 165 and 166: BIBLIOGRAPHY 143 [85] Lindeberg, T.
Page 167 and 168: BIBLIOGRAPHY 145 [108] O’Donnell,
Page 169 and 170: BIBLIOGRAPHY 147 [129] Schmugge, S.
Page 171 and 172: BIBLIOGRAPHY 149 [150] van Bemmel,
Page 173: BIBLIOGRAPHY 151 [171] Yi, J. and R
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