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

120 Chapter 7. Airway Tree Segmentation
to be unconnected in the image data. Contrary to region growing or front propagation
approaches, our approach identifies the unconnected airways and allows us to link them
together (Fig. 3.4(e)). This ability enables our method to handle local disturbances robustly.
Compared to other methods, a slightly increased leakage volume can be observed;
Fig. 7.8(a)-(c) depicts the three cases with the largest “leakage volume”. Two points can
be observed: first, the majority of the “leaks” detected by the evaluation framework are due
to surface representation inaccuracies (Figs. 7.8(c) and (d)) and second, blobs are included
in some airway segmentations (Figs. 7.8(a) and (b)). Our approach produces a description
of the airway tree on a structural level (centerline points, radius, tangent direction), but
not a voxel or sub-voxel accurate segmentation of the inner and/or outer airway wall(s).
The first point can be explained as we performed a dilation of the segmentation results to
assure 6-connectivity, which negatively influencs the performance evaluation. The second
point was later found to be related to a bug in the software part that transforms the
structural representation to a binary volume dataset.
Comparison between the two methods: We presented two approaches for airway
tree extraction from CT datasets with different properties. Regarding the evaluation, both
seem to perform comparably well on this database and the differences are more on the
qualitative side. While with the GVF based method shows a higher “branch count”, is
the “tree length” larger for the airway tree reconstruction method. The “leakage volume”
for the airway tree reconstruction method seems slightly larger. However, reasons for this
statical conclusion were discussed.
The GVF based approach produces a voxel-accurate segmentation of the airway lumen
while the airway tree reconstruction method only produces a structural representation
roughly describing the airways surfaces. An additional advantage of the GVF based methods
is that the presented centerline extraction method may be easily extended to extract a
complete high quality curve skeleton of the airway tree. This can be achieved by obtaining
connections between the individual centerlines based on the GVFs medialness property
using the method presented in Section 3.3. However, the GVF based method can not cope
with severe disturbances (e.g. the example with the tumor) and it does not avoid leakage
in case of emphysema as no structural verification of the airway tree is performed. Contrary
to that, the airway tree reconstruction method allows handling these disturbances
successfully.