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Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery

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Signal and Image Analysis for Biomedical and Life Sciences

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 823))

Abstract

This chapter presents an approach to processing ultra high-resolution, large-size biomedical imaging data for the purposes of detecting and quantifying vasculature and microvasculature . Capturing early signs of any changes in vasculature may have significant values for early-diagnosis and treatment assessment due to the well understood observation that vascular changes precede cancerous growth and metastasis . With the advent of key enabling technologies for extremely high-resolution imaging, such as synchrotron radiation based computed tomography (CT) , the required levels of detail have become accessible. However, these technologies also present challenges in data analysis. This chapter aims to offer some insights as to how these changes might be best dealt with. We argue that the necessary steps in quantitative understanding of vasculatures include targeted data enhancement , information reduction aimed at characterizing the linear structure of vessels , and quantitatively describing the vessel hierarchy. We present results on cerebral and liver vasculatures of a mouse captured at the Shanghai Synchrotron Radiation Facility (SSRF). These results were achieved with a processing pipeline comprising of our empirically selected component for each of the above steps. Towards the end, we discuss how alternative and additional components may be incorporated for improved speed and robustness.

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Acknowledgements

This research is supported by CSIRO – Chinese Academy of Sciences Collaborative Research Fund and CSIRO Computational and Simulation Sciences Transformational Capability Platform (CSS TCP). This work benefited from the use of the Insight Segmentation and Registration Toolkit (ITK) and the Visualization Toolkit (VTK), open source software developed as an initiative of the U.S. National Library of Medicine and available at www.itk.org.

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Authors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Locked Bag 17, North Ryde, Sydney, NSW, 1670, Australia

    Rongxin Li, Dadong Wang, Changming Sun & Ryan Lagerstrom

  2. Shanghai Synchrotron Radiation Facility (SSRF), Chinese Academy of Sciences, Shanghai Institute of Applied Physics, 239 Zhangheng Road, Pudong District, Shanghai, 201204, China

    Hai Tan, You He & Tiqiao Xiao

Authors
  1. Rongxin Li
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  2. Dadong Wang
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  3. Changming Sun
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  4. Ryan Lagerstrom
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  5. Hai Tan
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  6. You He
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  7. Tiqiao Xiao
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Corresponding author

Correspondence to Rongxin Li .

Editor information

Editors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Changming Sun

  2. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Tomasz Bednarz

  3. The University of Aizu, Fukushima, Japan

    Tuan D. Pham

  4. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Pascal Vallotton

  5. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Dadong Wang

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Li, R. et al. (2015). Towards Automated Quantitative Vasculature Understanding via Ultra High-Resolution Imagery. In: Sun, C., Bednarz, T., Pham, T., Vallotton, P., Wang, D. (eds) Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-10984-8_10

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