Branchpoint labeling and matching in human airway trees

Abstract

The functional understanding of the pulmonary anatomy as well as the tracking of the natural course of respiratory diseases are critically dependent on our ability to repeatedly evaluate the same region of the lungs time after time and perform accurate and reliable positionally corresponding measurements. We present a method for accurate labeling of airway branchpoints with their anatomical names as well as an approach for accurate matching of airway tree branchpoints beyond those with anatomical names. An intra-subject tree-matching as well as matching across subjects is achieved. The labeling process is based on matching against a population average. This population average incorporates the anatomical variability that is typically observed across the population. The matching algorithm is based on an association graph method. The computing time is drastically reduced by introducing a hierarchical splitting and only matching two sub-trees at a time. Both steps well tolerate possible false branches. Validation against an independent standard provided by human experts shows a high degree of accuracy ( > 90%) for both labeling and matching. The average error compares well to the inter-observer variability among human experts.