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Workshop on Clinical Image-Based Procedures

International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

CLIP 2019, UNSURE 2019: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures pp 85–93Cite as

A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11840))

Abstract

In this paper, a clinical measuring platform for quantifying nucleus-cells (MPQ-N-cells) at combining a novel color region-based segmentation strategy is proposed to accelerate the discovery of diseases diagnostically in medical imaging. In the approach, average values of colors in an image are employed as similarity criteria to assign image voxels to regions using the minimum distance classifier in the color region growing process. Then, the binary image transformation and graphic contour line procedure are performed, followed by the operation of region area calculation to obtain the actual numbers of voxels within the segmented patterns of the N-cells quantitatively. The proposed approach of MPQ-N-cells is implemented on the heterogeneous medical image datasets related to Parkinson disease, oculopharyngeal muscular dystrophy (one type of protein conformational diseases) and glioblastoma cancer. Implementation results reveal that the proposed MPQ-N-cells approach is capable of quantifying a variety of pathological N-cells clinically with improved data visualization in heterogeneous datasets. This study has the potential to lead to more successful measurement of cell diagnostically and further to track changes of cell in medical imaging for a longitudinal study on supporting the studies of disease.

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Author information

Authors and Affiliations

  1. Montreal, QC, Canada

    Peifang Guo

Authors
  1. Peifang Guo
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Corresponding author

Correspondence to Peifang Guo .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. University College London, London, UK

    Ryutaro Tanno

  3. Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore

    Marius Erdt

  4. McGill University, Montreal, QC, Canada

    Tal Arbel

  5. ETH Zürich, Zürich, Switzerland

    Christian Baumgartner

  6. Massachusetts Institute of Technology, Harvard Medical School, Cambridge, MA, USA

    Adrian Dalca

  7. University College London, King's College London, London, UK

    Carole H. Sudre

  8. Harvard Medical School, Boston, MA, USA

    William M. Wells

  9. Aachen University of Applied Sciences, Aachen, Germany

    Klaus Drechsler

  10. Children’s National Healthcare System, Washington, D.C., DC, USA

    Marius George Linguraru

  11. Fraunhofer IGD, Darmstadt, Germany

    Cristina Oyarzun Laura

  12. Children's National Healthcare System, Washington, D.C., DC, USA

    Raj Shekhar

  13. Fraunhofer IGD, Darmstadt, Germany

    Stefan Wesarg

  14. ICREA - Universitat Pompeu Fabra, Barcelona, Spain

    Miguel Ángel González Ballester

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Guo, P. (2019). A Clinical Measuring Platform for Building the Bridge Across the Quantification of Pathological N-Cells in Medical Imaging for Studies of Disease. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-32689-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32688-3

  • Online ISBN: 978-3-030-32689-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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