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SwifTree: Interactive Extraction of 3D Trees Supporting Gaming and Crowdsourcing

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Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2017, STENT 2017, CVII 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10552))

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Abstract

Analysis of vascular and airway trees of circulatory and respiratory systems is important for many clinical applications. Automatic segmentation of these tree-like structures from 3D data remains an open problem due to their complex branching patterns, geometrical diversity, and pathology. On the other hand, it is challenging to design intuitive interactive methods that are practical to use in 3D for trees with tens or hundreds of branches. We propose SwifTree, an interactive software for tree extraction that supports crowdsourcing and gamification. Our experiments demonstrate that: (i) aggregating the results of multiple SwifTree crowdsourced sessions achieves more accurate segmentation; (ii) using the proposed game-mode reduces time needed to achieve a pre-set tree segmentation accuracy; and (iii) SwifTree outperforms automatic segmentation methods especially with respect to noise robustness.

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Author information

Authors and Affiliations

  1. Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Mian Huang & Ghassan Hamarneh

Authors
  1. Mian Huang
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  2. Ghassan Hamarneh
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Corresponding author

Correspondence to Mian Huang .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    M. Jorge Cardoso

  2. McGill University, Montreal, Québec, Canada

    Tal Arbel

  3. Imperial College London, London, United Kingdom

    Su-Lin Lee

  4. Eindhoven University of Technology, Eindhoven, The Netherlands

    Veronika Cheplygina

  5. University of Barcelona, Barcelona, Spain

    Simone Balocco

  6. Technical University of Munich, Garching, Germany

    Diana Mateus

  7. Nara Institute of Science and Technology, Nara, Japan

    Guillaume Zahnd

  8. DKFZ, Heidelberg, Germany

    Lena Maier-Hein

  9. Technical University Munich, Munich, Germany

    Stefanie Demirci

  10. École de Technologie Supérieure, Montreal, Québec, Canada

    Eric Granger

  11. École de Technologie Supérieure, Montreal, Canada

    Luc Duong

  12. École de Technologie Supérieure, Montreal, Québec, Canada

    Marc-André Carbonneau

  13. Technical University Munich, Munich, Germany

    Shadi Albarqouni

  14. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

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Huang, M., Hamarneh, G. (2017). SwifTree: Interactive Extraction of 3D Trees Supporting Gaming and Crowdsourcing. In: Cardoso, M., et al. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS STENT CVII 2017 2017 2017. Lecture Notes in Computer Science(), vol 10552. Springer, Cham. https://doi.org/10.1007/978-3-319-67534-3_13

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  • DOI: https://doi.org/10.1007/978-3-319-67534-3_13

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  • Print ISBN: 978-3-319-67533-6

  • Online ISBN: 978-3-319-67534-3

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