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Novel Digital Pathology Method for Computer-Aided Analysis of Histopathological Images Obtained from Dystrophic Muscle Biopsies

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Current Trends in Biomedical Engineering and Bioimages Analysis (PCBEE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1033))

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Abstract

Background: Despite the introduction of a full range of genetic diagnostic tests and sophisticated techniques in modern pathology, interpretation of histopathological images obtained from muscle biopsies remains important in the daily practice of neuropathology since it can give indications of the severity and the rate of progression of neuromuscular disease. In this paper, we propose a simple and time saving method for quantitative assessment of severity of Duchenne Muscular Dystrophy (DMD) based on computer-aided analysis of histopathological images obtained from biopsies of dystrophic muscles.

Methods: The method that we propose, colour filtration pixel-by-pixel of the whole virtual slides (CFPP method), enables semi-quantitative evaluation of morphological structure of the muscular tissue. We retrospectively analyzed digital microscopic images of DMD muscle tissue from patients and healthy persons. The images were acquired with x400 magnification, from original microscopy slides coming from biopsies. For comparison, we constructed histograms of muscle fiber diameters with the areas of measurements selected randomly from the center of each section.

Results: CFPP method allows to distinguish DMD tissue from normal muscle tissue, as well as it makes possible to assess quantitatively the severity of DMD through assessment of connective tissue index, a proliferation index dedicated to the grading of DMD. The method required only choosing of small representative regions of muscle tissue, of fatty and connective tissue, and of nuclei.

Conclusions: Results demonstrate usefulness of the proposed method in neuropathological assessment of DMD severity. It is a promising technique that can assist tissue-based diagnosis and can be used for virtual slides evaluation.

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Abbreviations

CFPP method: :

Colour Filtration Pixel-by-Pixel method

CIH, CIC::

connective tissue indices

DMD: :

Duchenne Muscular Dystrophy

ILF method: :

Image Landscapes’ Fractal Dimension method

ROIs: :

regions of interests

VM: :

virtual microscopy

WSI: :

whole slide image

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Acknowledgements

This work was partially supported by Nalecz Institute of Biocybernetics and Biomedical Engineering Polish Academy of Sciences and by Warsaw Medical University statutory activities. It was also supported by COST Action BM1304 MYO-MRI.

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Authors and Affiliations

  1. Nalecz Institute of Biocybernetics and Biomedical Engineering PAS, Warsaw, Poland

    Wlodzimierz Klonowski & Bozenna Kuraszkiewicz

  2. Department of Neurology, Medical University of Warsaw, Warsaw, Poland

    Anna M. Kaminska & Anna Kostera-Pruszczyk

Authors
  1. Wlodzimierz Klonowski
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  2. Bozenna Kuraszkiewicz
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  3. Anna M. Kaminska
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  4. Anna Kostera-Pruszczyk
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Corresponding author

Correspondence to Wlodzimierz Klonowski .

Editor information

Editors and Affiliations

  1. Institute of Control and Computation Engineering, University of Zielona Gora, Zielona Góra, Poland

    Józef Korbicz

  2. Committee of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland

    Roman Maniewski

  3. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Krzysztof Patan

  4. Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland

    Marek Kowal

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Klonowski, W., Kuraszkiewicz, B., Kaminska, A.M., Kostera-Pruszczyk, A. (2020). Novel Digital Pathology Method for Computer-Aided Analysis of Histopathological Images Obtained from Dystrophic Muscle Biopsies. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-29885-2_11

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  • Print ISBN: 978-3-030-29884-5

  • Online ISBN: 978-3-030-29885-2

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