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

54 Chapter 3. Grouping and Linkage into Connected Networks
3.4 Experiments
In this section, we study the behavior of the previously presented grouping and linkage
methods (Section 3.2 and 3.3) with respect to the structural correctness of the tubular
tree structures and with respect to the accuracy of the extracted curve skeletons.
3.4.1 Structural Correctness
To study properties of the method’s abilities to obtain the correct structure, both methods
were applied for the task of airway tree reconstruction and liver vascular tree reconstruction/separation
(Figs. 3.3 and 3.4). The tubular structures were obtained from the
datasets using the GVF-based TDF with the offset medialness function as presented in
Section 2.3.2. Organ masks were utilized to discard tubular structures in the datasets.
For the structural tree reconstruction method (Section 3.2), the required root elements
were selected manually.
Ability to separate multiple interwoven tubular tree structures: Fig. 3.3 show
the results on a contrast enhanced liver CT dataset. Fig. 3.3(a) and (b) show the original
dataset and the extracted tubular structures respectively. The dataset contains the portal
vein tree and the hepatic vein trees. Both of them have a comparable gray value in the
datasets, and both appear overlapping in some areas due to partial voluming. The dataset
further shows a tumorous region adjacent to one of the vessels. As can be seen from the
extracted tubular structures, the TDF was able to suppress the tumorous region, however,
also the vessel adjacent to the tumor was not identified. Additionally, the centerlines
between the tubular structures break up in some areas (e.g. branching areas). Fig. 3.3(c)
and (d) show the results of the image based and the structure based approach, respectively,
showing all tubular structures identified as part of the portal vein tree in blue. As can be
seen, the image based approach was able to close the gap in proximity of the tumor, but
it was not able to distinguish between the two vascular tree structures as both overlap in
the image domain. Using the presented structure based approach avoids this problem and
allows for a correct reconstruction and separation of the vascular trees.
Impact of flow direction information on structural correctness: Contrary to
other structure based approaches that perform a grouping of unconnected tubular structures
into complete tree structures, our method incorporates flow direction information
(Section 3.2). To assess the impact of this additional information on robustness, we re-