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

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116 Chapter 7. Airway Tree Segmentation sented methods on the 20 testing datasets and for comparison results achieved with other methods. The quantitative measures were provided by the “Extraction of Airways from CT 2009 (EXACT09)” database and are available online at http://image.diku.dk/exact. 7.3.1 Results of Proposed Methods Tables 7.1 and 7.2 summarize the evaluation results for the 20 testing datasets of both methods. For the GVF based method (Table 7.1), on average 63.0% of airway branches were detected with an average detected tree length of 58.4%. The mean leakage count was 5.0, and the mean false positive rate was 1.44% (median: 0.61%). For the airway tree reconstruction method (Table 7.2), on average, 57.9% of airway branches were detected with an average detected tree length of 55.2%. The mean leakage count was 6.5, and the mean false positive rate was 2.44% (median: 1.41%). 7.3.2 Comparison to Other Methods A larger set of other methods [13, 35, 36, 38, 57, 76, 87, 100, 112, 147, 154, 158, 160] has been evaluated on the “Extraction of Airways from CT 2009 (EXACT09)” database. The results achieved with these methods are summarized in Table 7.3 and Fig. 7.5. We refrain from detailed discussions of all other methods referring for this purpose to the work of Lo et al. [88] and only provide a short comparison of our proposed method with the other methods. Most methods for airway tree segmentation are fully automated methods, or they are semi-automated methods that require only minimal user interaction such as selecting initial seed points inside the trachea or adaption of parameters. An exception is the method of Tschirren et al. [147] that is part of a clinical product. Their software allows for extensive editing of an initial segmentation result. What all other methods have in common, is that they use region-growing or wave propagation approaches for extraction of the airway trees. The methods evolve starting from a seed inside trachea and recursively merge potential airway regions, usually combined with a method for leakage prevention. A large variation between the performance of the different methods can be observed as well as a general trade-off between extraction capability and leakage as shown in Fig. 7.5. Methods with higher branch count also show a higher leakage volume and vice versa. The better methods show an ability to extract about 55-60% of the airway tree with a false positive rate of below 5%. The method with the highest extraction capability also allows extraction of only about 75%. However, to the cost of a false positive rate of more than
7.3. Evaluation and Results 117 Table 7.1: Evaluation results for the GVF based method (Section 7.2.1) on the twenty test cases. Branch Branch Tree Tree length Leakage Leakage False count detected length detected count volume positive (%) (cm) (%) (mm 3 ) rate (%) CASE21 114 57.3 68.5 62.0 3 35.2 0.31 CASE22 270 69.8 218.9 66.2 9 474.6 1.60 CASE23 187 65.8 134.0 51.5 5 43.5 0.20 CASE24 139 74.7 113.9 70.0 8 176.9 0.59 CASE25 158 67.5 123.1 48.8 4 98.6 0.32 CASE26 59 73.8 51.2 78.0 2 274.3 2.70 CASE27 77 76.2 58.1 71.7 4 353.5 3.06 CASE28 86 69.9 66.9 61.0 1 49.6 0.43 CASE29 120 65.2 81.2 58.8 4 118.9 0.85 CASE30 114 58.5 87.5 57.2 5 98.9 0.64 CASE31 96 44.9 70.5 40.2 1 59.8 0.34 CASE32 101 43.3 80.3 36.8 1 175.2 0.86 CASE33 117 69.6 90.4 61.5 1 32.0 0.29 CASE34 250 54.6 184.6 51.6 11 358.1 1.05 CASE35 168 48.8 110.9 35.9 5 69.8 0.30 CASE36 294 80.8 330.6 80.2 5 78.6 0.25 CASE37 112 60.5 87.9 49.5 2 102.9 0.48 CASE38 64 65.3 51.2 77.1 4 311.0 2.64 CASE39 291 56.0 250.6 61.2 13 3577.0 9.21 CASE40 225 57.8 187.7 48.5 11 959.0 2.65 Mean 152.1 63.0 122.4 58.4 5.0 372.4 1.44 Std. dev. 75.7 10.4 75.2 13.2 3.6 785.4 2.06 Min 59 43.3 51.2 35.9 1 32.0 0.20 1st quartile 96 56.0 68.5 48.8 2 59.8 0.31 Median 119 65.3 89.2 59.9 4 110.9 0.61 3rd quartile 250 73.8 187.7 71.7 9 358.1 2.65 Max 294 80.8 330.6 80.2 13 3577.0 9.21 15%. None of the methods comes close to obtaining the whole airway tree. The method of Graham et al. [50] who also presented an airway tree extraction approach based on a bottom-up airway detection with a consecutive grouping into a complete tree structure as described in the introduction has not been evaluated on the database.
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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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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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Segmentation of 3D Tubular Tree Structures in Medical Images ...

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