Extraction of airway trees using multiple hypothesis tracking and template matching

TL;DR

This paper introduces an automatic airway tree extraction method from chest CT images using multiple hypothesis tracking and template matching, improving robustness and performance over previous approaches.

Contribution

The method adapts vessel segmentation techniques for airway extraction, removing local thresholding and enhancing decision-making through statistical hypothesis tree traversal.

Findings

Improved performance over original semi-automatic method

Comparable results to region growing on probability images

Less sensitivity to local anomalies in data

Abstract

Knowledge of airway tree morphology has important clinical applications in diagnosis of chronic obstructive pulmonary disease. We present an automatic tree extraction method based on multiple hypothesis tracking and template matching for this purpose and evaluate its performance on chest CT images. The method is adapted from a semi-automatic method devised for vessel segmentation. Idealized tubular templates are constructed that match airway probability obtained from a trained classifier and ranked based on their relative significance. Several such regularly spaced templates form the local hypotheses used in constructing a multiple hypothesis tree, which is then traversed to reach decisions. The proposed modifications remove the need for local thresholding of hypotheses as decisions are made entirely based on statistical comparisons involving the hypothesis tree. The results show improvements in performance when compared to the original method and region growing on intensity images. We also compare the method with region growing on the probability images, where the presented method does not show substantial improvement, but we expect it to be less sensitive to local anomalies in the data.