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Automatic Detection of Histological Artifacts in Mouse Brain Slice Images

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Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging (BAMBI 2016, MCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10081))

Abstract

A major challenge in automatic registration, alignment and 3-D reconstruction of conventionally processed mouse brain slice images is the presence of histological artifacts, like tissue tears and losses. These artifacts are often produced from manual sample preparation processes, which are ubiquitous in most neuroanatomical laboratories. We present a novel geometric algorithm to automatically detect these artifacts (damage regions) in mouse brain slice images. Our algorithm is guided by our observation that the tears and tissue loss in brain slice images result in external geometric medial axis of the outer contours to go deep inside the tissue. We tested our algorithm on 52 mouse brain slice images with major histological artifacts and successfully detected all the damage regions in the dataset. Our algorithm also demonstrated much lower errors when quantitatively evaluated by performing feature based registration between all 52 slices and their corresponding Allen Reference Atlas (ARA) images.

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Notes

  1. 1.

    Publically available from the Allen Brain Atlas Project.

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Acknowledgement

The authors would like to thank Dr Hong-Wei Dong for providing the mouse brain atlas contour images. This work was supported in part by NIH grants (R01MH105427 and R01NS078434).

Author information

Authors and Affiliations

  1. Interactive Graphics & Visualization Laboratory, Department of Computer Science, University of California, Irvine, CA, USA

    Nitin Agarwal & M. Gopi

  2. Department of Anatomy & Neurobiology, University of California, Irvine, CA, USA

    Xiangmin Xu

Authors
  1. Nitin Agarwal
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  2. Xiangmin Xu
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  3. M. Gopi
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Corresponding author

Correspondence to Nitin Agarwal .

Editor information

Editors and Affiliations

  1. HES-SO, Sierre, Switzerland

    Henning Müller

  2. Siemens AG, Munich, Germany

    B. Michael Kelm

  3. McGill University, Montreal, Québec, Canada

    Tal Arbel

  4. University of Sydney, Sydney, New South Wales, Australia

    Weidong Cai

  5. University College London, London, United Kingdom

    M. Jorge Cardoso

  6. Medical University of Vienna, Vienna, Austria

    Georg Langs

  7. Technische Universität München, Garching, Germany

    Bjoern Menze

  8. Rutgers The State University of New Jersey, Piscataway, New Jersey, USA

    Dimitris Metaxas

  9. University of Texas Southwestern Medical Center, Dallas, Texas, USA

    Albert Montillo

  10. Brigham and Women’s Hospital, Boston, Massachusetts, USA

    William M. Wells III

  11. University of North Carolina at Charlotte, Charlotte, North Carolina, USA

    Shaoting Zhang

  12. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Albert C.S. Chung

  13. University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom

    Mark Jenkinson

  14. IcoMetrix, Leuven, Belgium

    Annemie Ribbens

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Agarwal, N., Xu, X., Gopi, M. (2017). Automatic Detection of Histological Artifacts in Mouse Brain Slice Images. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-61188-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61187-7

  • Online ISBN: 978-3-319-61188-4

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