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A Novel Iterative Method for Airway Tree Segmentation from CT Imaging Using Multiscale Leakage Detection

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

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Abstract

Computed tomography (CT)-based metrics of airway phenotypes, wall-thickness, and other morphological features are increasingly being used in large multi-center lung studies involving many hundreds or thousands of image datasets. There is an unmet need for a fully reliable, automated algorithm for CT-based segmentation of airways. State-of-the-art methods require a post-editing step, which is time consuming when several thousands of image data sets need to be reviewed and edited. In this paper, we present a novel iterative algorithm for CT-based segmentation of airway trees. Early testing suggests that the method requires no editing to extract a set of airway segments along a standardized set of bronchial paths extending two generations beyond the segmental airways. It uses simple intensity-based connectivity and new leakage detection and volume freezing algorithms to iteratively grow an airway tree. It starts with an initial, automatically determined seed inside the trachea and a conservative threshold; applies region growing and generates a leakage-corrected segmentation; freezes the segmented volume; and shifts the threshold toward a more generous value for the next iteration until a convergence occurs. The method was applied on chest CT scans of fifteen normal non-smoking subjects. Airway segmentation results were compared with manually edited results, and branch level accuracy of the new segmentation method was examined along five standardized segmental airway paths and continuing to two generations beyond the segmental paths. The method successfully detected all branches up to two generations beyond the five segmental airway paths with no visual leakages.

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Acknowledgements

This work was supported by the NIH grant R01 HL112986.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, 52242, USA

    Syed Ahmed Nadeem, Dakai Jin & Punam K. Saha

  2. Department of Radiology, University of Iowa, Iowa City, Iowa, 52242, USA

    Eric A. Hoffman & Punam K. Saha

Authors
  1. Syed Ahmed Nadeem
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Correspondence to Syed Ahmed Nadeem .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Nadeem, S.A., Jin, D., Hoffman, E.A., Saha, P.K. (2017). A Novel Iterative Method for Airway Tree Segmentation from CT Imaging Using Multiscale Leakage Detection. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_4

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