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

56 Chapter 3. Grouping and Linkage into Connected Networks
tubular tree structures considerably.
Behavior in case of disturbances and significant deviations from a typical tubular
shape: Fig. 3.4 shows an airway tree containing a large tumor that infiltrates the
airways. The tumor blocks the connection to one of the airway branches completely and
leads to a stenosis in another airway branch (Fig. 3.4(a) and (b)). As can be seen from the
extracted tubular structures (Fig. 3.4(c)) both parts were not identified by the TDF. The
result of applying the structure based and the image based grouping/linkage approaches
on this datasets results in the extracted structures shown in Fig. 3.4(d) and (e). The image
based approach was able to extract a valid link in case of the stenosis. However, it was
not able to identify a connection to the completely blocked airway branch. The structure
based approach on the other hand was able to cope with all these severe disturbances.
3.4.2 Centerline Accuracy
In this section, we study the achievable centerline accuracy with previously presented
grouping and linkage methods. We present results achieved on clinical datasets and compare
them quantitatively and qualitatively to other methods with similar objectives. We
will show two things. First, our presented image based approach that utilizes the properties
of the GVF (Section 3.3) is able to extract medial curves that stay centered in
complicated cases where conventional TDFs and non-image based linkage strategies have
problems. We do this by comparing the results achieved with the GVF-based approach
to results achieved with the structure based approach and to the results achieved with
another approach by Krissian et al. [18, 70] that extracts medial curves directly from the
gray value images . Second, the accuracy of the centerlines extracted with the GVF-based
approach is comparable to those achieved with pure skeletonization approaches from accurate
segmentations. We do this by comparing the results of our GVF-based approach to
the skeletons extracted with three different skeletonization approaches [15, 53, 110] from
known gold standards (available segmentations of the interesting structures).
Datasets and methods: The two clinical datasets we use for evaluation show an airway
tree (see Fig. 3.5) and a contrast CT of an aorta containing a severe stenosis due
to calcification (see Fig. 3.6). Segmentations of the bronchial tree and the aorta were
available. The segmentation of the aorta follows the interior of the aorta excluding the
calcifications.