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3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images

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Book cover Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment (RAMBO 2017, CMMI 2017, SWITCH 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10555))

Abstract

Positron emission tomography – computed tomography (PET-CT) has been widely used in modern cancer imaging. Accurate tumor delineation from PET and CT plays an important role in radiation therapy. The PET-CT co-segmentation technique, which makes use of advantages of both modalities, has achieved impressive performance for tumor delineation. In this work, we propose a novel 3D image matting based semi-automated co-segmentation method for tumor delineation on dual PET-CT scans. The “matte” values generated by 3D image matting are employed to compute the region costs for the graph based co-segmentation. Compared to previous PET-CT co-segmentation methods, our method is completely data-driven in the design of cost functions, thus using much less hyper-parameters in our segmentation model. Comparative experiments on 54 PET-CT scans of lung cancer patients demonstrated the effectiveness of our method.

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Acknowledgments

This research was supported in part by the NIH Grant R21CA209874.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Iowa, 4016 Seamans Center, Iowa City, IA, 52242, USA

    Zisha Zhong & Xiaodong Wu

  2. Department of Radiation Oncology, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52242, USA

    Yusung Kim, John Buatti & Xiaodong Wu

Authors
  1. Zisha Zhong
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  2. Yusung Kim
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  3. John Buatti
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  4. Xiaodong Wu
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Corresponding author

Correspondence to Zisha Zhong .

Editor information

Editors and Affiliations

  1. University College London , London, United Kingdom

    M. Jorge Cardoso

  2. McGill University , Montreal, Québec, Canada

    Tal Arbel

  3. Siemens Medical Solutions, Knoxville, Tennessee, USA

    Fei Gao

  4. Imperial College London , London, United Kingdom

    Bernhard Kainz

  5. Biomedical Imaging Group, Rotterdam, The Netherlands

    Theo van Walsum

  6. Technical University Munich , Munich, Germany

    Kuangyu Shi

  7. Visulytix Limited , London, United Kingdom

    Kanwal K. Bhatia

  8. Biomedical Imaging Group, Rotterdam, The Netherlands

    Roman Peter

  9. University College London, London, United Kingdom

    Tom Vercauteren

  10. University of Bern , Bern, Switzerland

    Mauricio Reyes

  11. Harvard Medical School , Boston, Massachusetts, USA

    Adrian Dalca

  12. University Hospital of Bern , Bern, Switzerland

    Roland Wiest

  13. Biomedical Imaging Group, Rotterdam, The Netherlands

    Wiro Niessen

  14. Academic Medical Center , Amsterdam, The Netherlands

    Bart J. Emmer

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Zhong, Z., Kim, Y., Buatti, J., Wu, X. (2017). 3D Alpha Matting Based Co-segmentation of Tumors on PET-CT Images. In: Cardoso, M., et al. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. RAMBO CMMI SWITCH 2017 2017 2017. Lecture Notes in Computer Science(), vol 10555. Springer, Cham. https://doi.org/10.1007/978-3-319-67564-0_4

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  • DOI: https://doi.org/10.1007/978-3-319-67564-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67563-3

  • Online ISBN: 978-3-319-67564-0

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