Extraction of airway trees using multiple hypothesis tracking and template matching

Selvan Raghavendra; Jens Petersen; Jesper Johannes Holst Pedersen; Marleen de Bruijne

Extraction of airway trees using multiple hypothesis tracking and template matching

Selvan Raghavendra, Jens Petersen, Jesper Johannes Holst Pedersen, Marleen de Bruijne

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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.

Original language	English
Title of host publication	The Sixth International Workshop on Pulmonary Image Analysis : Athens, Greece - October 21, 2016
Editors	Reinhard R. Beichel, Keyvan Farahani, Colin Jacobs, Sven Kabus, Atilla P. Kiraly, Jan-Martin Kuhnigk, Jamie R. McClelland, Kensaku Mori, Jens Petersen, Simon Rit
Number of pages	12
Publisher	Create Space Independent Publishing Platform
Publication date	2016
Pages	43-54
ISBN (Print)	978-1-5370-3858-2
Publication status	Published - 2016
Event	6th International Workshop on Pulmonary Image Analysis - Athen, Greece Duration: 21 Oct 2016 → 21 Oct 2016 Conference number: 6

Conference

Conference	6th International Workshop on Pulmonary Image Analysis
Number	6
Country/Territory	Greece
City	Athen
Period	21/10/2016 → 21/10/2016

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ExtractionOfAirwayTreesUsingMHT_cameraReadySubmitted manuscript, 3.07 MB

https://arxiv.org/abs/1611.08131

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Raghavendra, S., Petersen, J., Pedersen, J. J. H., & de Bruijne, M. (2016). Extraction of airway trees using multiple hypothesis tracking and template matching. In R. R. Beichel, K. Farahani, C. Jacobs, S. Kabus, A. P. Kiraly, J.-M. Kuhnigk, J. R. McClelland, K. Mori, J. Petersen, & S. Rit (Eds.), The Sixth International Workshop on Pulmonary Image Analysis: Athens, Greece - October 21, 2016 (pp. 43-54). Create Space Independent Publishing Platform. https://arxiv.org/abs/1611.08131

Extraction of airway trees using multiple hypothesis tracking and template matching. / Raghavendra, Selvan ; Petersen, Jens; Pedersen, Jesper Johannes Holst et al.
The Sixth International Workshop on Pulmonary Image Analysis: Athens, Greece - October 21, 2016. ed. / Reinhard R. Beichel; Keyvan Farahani; Colin Jacobs; Sven Kabus; Atilla P. Kiraly; Jan-Martin Kuhnigk; Jamie R. McClelland; Kensaku Mori; Jens Petersen; Simon Rit. Create Space Independent Publishing Platform, 2016. p. 43-54.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Raghavendra, S , Petersen, J, Pedersen, JJH & de Bruijne, M 2016, Extraction of airway trees using multiple hypothesis tracking and template matching. in RR Beichel, K Farahani, C Jacobs, S Kabus, AP Kiraly, J-M Kuhnigk, JR McClelland, K Mori, J Petersen & S Rit (eds), The Sixth International Workshop on Pulmonary Image Analysis: Athens, Greece - October 21, 2016. Create Space Independent Publishing Platform, pp. 43-54, 6th International Workshop on Pulmonary Image Analysis, Athen, Greece, 21/10/2016. <https://arxiv.org/abs/1611.08131>

Raghavendra S , Petersen J, Pedersen JJH, de Bruijne M. Extraction of airway trees using multiple hypothesis tracking and template matching. In Beichel RR, Farahani K, Jacobs C, Kabus S, Kiraly AP, Kuhnigk JM, McClelland JR, Mori K, Petersen J, Rit S, editors, The Sixth International Workshop on Pulmonary Image Analysis: Athens, Greece - October 21, 2016. Create Space Independent Publishing Platform. 2016. p. 43-54

Raghavendra, Selvan ; Petersen, Jens ; Pedersen, Jesper Johannes Holst et al. / Extraction of airway trees using multiple hypothesis tracking and template matching. The Sixth International Workshop on Pulmonary Image Analysis: Athens, Greece - October 21, 2016. editor / Reinhard R. Beichel ; Keyvan Farahani ; Colin Jacobs ; Sven Kabus ; Atilla P. Kiraly ; Jan-Martin Kuhnigk ; Jamie R. McClelland ; Kensaku Mori ; Jens Petersen ; Simon Rit. Create Space Independent Publishing Platform, 2016. pp. 43-54

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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.",

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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

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BT - The Sixth International Workshop on Pulmonary Image Analysis

A2 - Beichel, Reinhard R.

A2 - Farahani, Keyvan

A2 - Jacobs, Colin

A2 - Kabus, Sven

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A2 - Kuhnigk, Jan-Martin

A2 - McClelland, Jamie R.

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ER -

Extraction of airway trees using multiple hypothesis tracking and template matching

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