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

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LIST OF FIGURES
4.2 Shape prior for the constrained graph cut segmentation. . . . . . . . . . . . 69
4.3 Segmentation of an airway tree. . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.4 Segmentation of a diseased abdominal aorta in a contrast enhanced CT
dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.5 Segmentation of an airway tree. . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.1 Segmentation of liver vasculature trees from a contrast CT dataset with
intermediate processing results. . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 Rigid plastic “vessel” tree and image slices of the resulting phantom datasets
for varying backgrounds and scanning resolutions. . . . . . . . . . . . . . . 82
5.3 Percentage of undetected tubes (false negatives) for varying contrast situations,
scan resolutions, and tube diameters. . . . . . . . . . . . . . . . . . . 84
5.4 Segmentation error (relative tube diameter error) for varying contrast situations,
scan resolutions, and tube diameters. . . . . . . . . . . . . . . . . . . 85
5.5 Interactive visualization for evaluation of liver vasculature tree segmentation. 87
5.6 Unsegmented vessel (arrow) found by the radiologist. . . . . . . . . . . . . 89
5.7 Unsuccessfully detected portal artery. . . . . . . . . . . . . . . . . . . . . . 89
5.8 Successful segmentation of poorly contrasted hepatic vessels (arrow). . . . . 90
5.9 Successful segmentation of vessels in close proximity of a bright tumor. . . . 90
5.10 Wrong vessel connection identified by radiologist. . . . . . . . . . . . . . . . 91
5.11 Relation between image contrast and length of the extracted portal veins
and hepatic veins of the liver. . . . . . . . . . . . . . . . . . . . . . . . . . . 91
5.12 Separation and segmentation of liver vessel trees in a contrast enhanced CT
dataset with different methods. . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.1 Centerline extraction and vascular tree reconstruction. . . . . . . . . . . . . 99
6.2 Tube detection step on coronary CT. . . . . . . . . . . . . . . . . . . . . . . 101
6.3 Extracted coronary artery trees and provided reference centerlines. . . . . . 103
6.4 Centerline extraction at the proximal end of the conoary artery containing
a calcification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7.1 Example of initial vector field magnitude and GVF field magnitude on a
thorax CT dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.2 Intermediate results of the GVF-based tube centerline extraction method. . 112
7.3 Inverse gradient flow tracking tube segmentation applied to extracted tubular
structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
7.4 Illustration of the processing steps of the airway tree reconstruction approach.114
7.5 “Tree length detected” vs. “false positive rate” for different airway tree extraction
methods based on [88]. To ascribe the individual methods numbers
to the corresponding publication see Table 7.3. . . . . . . . . . . . . . . . . 119
7.6 Examples of segmentation results on the EXACT09 database using the
GVF-based method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123