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International Workshop on Prostate Cancer Imaging

Prostate Cancer Imaging 2010: Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention pp 15–24Cite as

Prostate Cancer Segmentation Using Multispectral Random Walks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6367))

Abstract

Several studies have shown the advantages of multispectral magnetic resonance imaging (MRI) as a noninvasive imaging technique for prostate cancer localization. However, a large proportion of these studies are with human readers. There is a significant inter and intra-observer variability for human readers, and it is substantially difficult for humans to analyze the large dataset of multispectral MRI. To solve these problems a few studies were proposed for fully automated cancer localization in the past. However, fully automated methods are highly sensitive to parameter selection and often may not produce desirable segmentation results. In this paper, we present a semi-supervised segmentation algorithm by extending a graph based semi-supervised random walker algorithm to perform prostate cancer segmentation with multispectral MRI. Unlike classical random walker which can be applied only to dataset of single type of MRI, we develop a new method that can be applied to multispectral images. We prove the effectiveness of the proposed method by presenting the qualitative and quantitative results of multispectral MRI datasets acquired from 10 biopsy-confirmed cancer patients. Our results demonstrate that the multispectral MRI noticeably increases the sensitivity and jakkard measures of prostate cancer localization compared to single MR images; 0.71 sensitivity and 0.56 jakkard for multispectral images compared to 0.51 sensitivity and 0.44 jakkard for single MR image based segmentation.

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Author information

Authors and Affiliations

  1. Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL, USA

    Yusuf Artan & Imam Samil Yetik

  2. Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Toronto, Ontario, Canada

    Masoom A. Haider

Authors
  1. Yusuf Artan
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  2. Masoom A. Haider
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  3. Imam Samil Yetik
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Editor information

Editors and Affiliations

  1. Department of Biomedical Engineering, Rutgers University, 599 Taylor Road, 08854, Piscataway, NJ, USA

    Anant Madabhushi

  2. CSIRO Australian e-Health Research Centre, Royal Brisbane and Women’s Hospital, 4029, Herston, QLD, Australia

    Jason Dowling

  3. Philips Research North America, 345 Scarborough Road, 10510, Briarcliff Manor, NY, USA

    Pingkun Yan

  4. Imaging Research Laboratories, Robarts Research Institute, 100 Perth Drive, N6A 5K8, London, Ontario, Canada

    Aaron Fenster

  5. Department of Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, V6T 1Z4, Vancouver, BC, Canada

    Purang Abolmaesumi

  6. Brigham and Women’s Hospital and Harvard Medical School, 75 Francis, 02115, St., Boston, MA, USA

    Nobuhiko Hata

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© 2010 Springer-Verlag Berlin Heidelberg

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Artan, Y., Haider, M.A., Yetik, I.S. (2010). Prostate Cancer Segmentation Using Multispectral Random Walks. In: Madabhushi, A., Dowling, J., Yan, P., Fenster, A., Abolmaesumi, P., Hata, N. (eds) Prostate Cancer Imaging. Computer-Aided Diagnosis, Prognosis, and Intervention. Prostate Cancer Imaging 2010. Lecture Notes in Computer Science, vol 6367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15989-3_3

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  • DOI: https://doi.org/10.1007/978-3-642-15989-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15988-6

  • Online ISBN: 978-3-642-15989-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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