Extraction of airway trees using multiple hypothesis tracking and template matching

TY - GEN

T1 - Extraction of airway trees using multiple hypothesis tracking and template matching

AU - Raghavendra, Selvan

AU - Petersen, Jens

AU - Pedersen, Jesper Johannes Holst

AU - de Bruijne, Marleen

N1 - Conference code: 6

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

M3 - Article in proceedings

SN - 978-1-5370-3858-2

SP - 43

EP - 54

BT - The Sixth International Workshop on Pulmonary Image Analysis

A2 - Beichel, Reinhard R.

A2 - Farahani, Keyvan

A2 - Jacobs, Colin

A2 - Kabus, Sven

A2 - Kiraly, Atilla P.

A2 - Kuhnigk, Jan-Martin

A2 - McClelland, Jamie R.

A2 - Mori, Kensaku

A2 - Petersen, Jens

A2 - Rit, Simon

PB - Create Space Independent Publishing Platform

T2 - 6th International Workshop on Pulmonary Image Analysis

Y2 - 21 October 2016 through 21 October 2016

ER -