Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
This work addresses the challenge of accurate tree segmentation in medical imaging for applications like airway and artery analysis, representing an incremental improvement over existing methods.
The paper tackled the problem of extracting tree structures like airways and coronary arteries from CT data by adapting a multiple hypothesis tracking method to handle scale-dependent parameters, resulting in significantly better performance than the original method on chest CT datasets.
In this work, we adapt a method based on multiple hypothesis tracking (MHT) that has been shown to give state-of-the-art vessel segmentation results in interactive settings, for the purpose of extracting trees. Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are used to construct the MHT tree, which is then traversed to make segmentation decisions. However, some critical parameters in this method are scale-dependent and have an adverse effect when tracking structures of varying dimensions. We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters. This enables our method to track trees starting from a single seed point. Our method is evaluated on chest CT data to extract airway trees and coronary arteries. In both cases, we show that our method performs significantly better than the original MHT method.