Point based methods for automatic bronchial tree matching and labeling

When addressing the task of automatic tracheo-bronchial tree matching it seems natural to make use of the tree's graph structure and branching characteristics. In tracheo-bronchial trees that were automatically extracted from multi-slice CT data, however, the graph information is not always reliable, especially for noisy or low-dose data which makes the abovementioned class of approaches prone to error in these situations. In this work we investigate what can be gained by using the spatial position of the bronchial centerline points. For this purpose we introduce, investigate, and compare two approaches to tree matching that are based on the use of centerline point positions alone with no additional connectivity information. As features we use (1) the 3D shape context and (2) statistical moments of the local point distribution. The 3D shape context has recently been introduced as a regional shape descriptor. It is based on a spherical histogram with logarithmic sampling in the radial direction. Both methods are used in order to match an automatically extracted tree to a manually labeled model tree which results in an automatic anatomical labeling of the data tree. Six tracheo-bronchial trees were matched to a given model tree. The data trees covered a range from high quality data to poor quality data. Furthermore two of the cases exhibited strongly distorted anatomy. It could be shown that the 3D shape context feature labeled 69 % of the branches correctly with one of 34 anatomical labels. In the case of the statistical moment feature 40 % of the branches were labeled correctly. We conclude that the set of centerline points alone allows correct labeling of a large portion of lung segments. We propose to combine the valuable local shape information in future work with connectivity and branching information, where the latter is reliably available.