Extracting tree structures in CT data by tracking multiple statistically ranked hypotheses

Research output: Contribution to journalJournal articleResearchpeer-review

PURPOSE: 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.

METHODS: Regularly spaced tubular templates are fit to image data forming local hypotheses. These local hypotheses are then used to construct the MHT tree, which is then traversed to make segmentation decisions. Some critical parameters in the method, we base ours on, 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.

RESULTS: The proposed method is evaluated on chest computed tomography data to extract airway trees and coronary arteries and compared to relevant baselines. In both cases, we show that our method performs significantly better than the Original MHT method in semiautomatic setting.

CONCLUSIONS: The statistical ranking of local hypotheses introduced allows the MHT method to be used in noninteractive settings yielding competitive results for segmenting tree structures.

Original languageEnglish
JournalMedical Physics
ISSN0094-2405
DOIs
Publication statusE-pub ahead of print - 15 Jul 2019

Bibliographical note

© 2019 American Association of Physicists in Medicine.

ID: 225920173