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Automated Assessment of Hematoma Volume of Rodents Subjected to Experimental Intracerebral Hemorrhagic Stroke by Bayes Segmentation Approach

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Abstract

Simulating a clinical condition of intracerebral hemorrhage (ICH) in animals is key to research on the development and testing of diagnostic or treatment strategies for this high-mortality disease. In order to study the mechanism, pathology, and treatment for hemorrhagic stroke, various animal models have been developed. Measurement of hematoma volume is an important assessment parameter to evaluate post-ICH outcomes. However, due to tissue preservation conditions and variables in digitization, quantification of hematoma volume is usually labor intensive and sometimes even subjective. The objective of this study is to develop an automated method that can accurately and efficiently obtain unbiased cerebral hematoma volume. We developed an application (MATLAB program) that can delineate the brain slice from the background and use the Hue information in the Hue/Saturation/Value (HSV) color space to segment the hematoma region. The segmentation threshold of Hue is calculated based on the Bayes classifier theorem so that the minimum error is mathematically ensured and automated processing is enabled. To validate the developed method, we compared the outcomes from the developed method with the hemoglobin content by the spectrophotometric assay method. The results were linearly correlated with statistical significance. The method was also validated by digital phantoms with an error less than 5% compared with the ground truth from the phantoms. Hematoma volumes yielded by the automated processing and those obtained by the operator’s manual operation are highly correlated. This automated segmentation approach can be potentially used to quantify hemorrhagic outcomes in rodent stroke models in an unbiased and efficient way.

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Acknowledgments

This study was partially sponsored by the National Institutes of Health (grant number NS094896). Authors are grateful to Dr. Jian Wang’s Laboratory of Johns Hopkins University for providing images of mice.

Funding

This study was partially funded by the National Institutes of Health (grant number NS094896).

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL, 33146, USA

    Zhexuan Zhang & Weizhao Zhao

  2. Peritz Scheinberg Cerebral Vascular Disease Research Laboratories, Department of Neurology, University of Miami, Miller School of Medicine, 1420 NW 9th Ave., Miami, FL, 33136, USA

    Sunjoo Cho, Ashish K. Rehni, Hever Navarro Quero, Kunjan R. Dave & Weizhao Zhao

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  1. Zhexuan Zhang
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  2. Sunjoo Cho
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Correspondence to Kunjan R. Dave or Weizhao Zhao.

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The authors declare that they have no conflicts of interest.

Ethics Approval

Experimental procedures on animals were carried out as per the Guide for the Care and Use of Laboratory Animals laid down by the National Institutes of Health and in accordance with the protocols approved by the Institutional Animal Care and Use Committee of the University of Miami. This article does not contain any studies with human participants performed by any of the authors.

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Zhang, Z., Cho, S., Rehni, A.K. et al. Automated Assessment of Hematoma Volume of Rodents Subjected to Experimental Intracerebral Hemorrhagic Stroke by Bayes Segmentation Approach. Transl. Stroke Res. 11, 789–798 (2020). https://doi.org/10.1007/s12975-019-00754-3

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  • DOI: https://doi.org/10.1007/s12975-019-00754-3

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