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Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools

Received: 27 December 2017     Published: 28 December 2017
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Abstract

In recent years, with the deepening of brain science research, mouse magnetic resonance microscopy (MRM) for neuroimaging research has gradually become a main research interest. Brain segmentation is an essential techniques for investigating the brain morphometry, while the traditional approach to segment a given brain involves the manual delineation of the ROIs by an expert. This practice can be slow and unscalable. Although automatic atlas-based segmentation approaches have been developed and validated for the human brain MRI, there is limited work for the mouse brain MRM. This paper combined optimized image registration and multi-atlas model for mouse brain segmentation. The results showed that multiple atlases with optimized geodesic-SyN can best improve the segmentation accuracy in the mouse brain, and registration algorithm plays important role in performance improvement.

Published in Science Discovery (Volume 5, Issue 6)
DOI 10.11648/j.sd.20170506.26
Page(s) 486-491
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Mouse, Magnetic Resource Microscopy, Brain Segmentation

References
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[5] 林岚,付振荣,张柏雯,等.PS/APP双转基因小鼠大脑中β-淀粉样蛋白的磁共振显微成像和组织切片研究[J].中国医疗设备,2016,31(2):31-33。
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[9] B. B. Avants,N. J. Tustison, G. Song, P. A. Cook,A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration,”J. Neuroimage, 54(3), 2011, pp. 2033-2044.
[10] Z. Fu, L. Lin, M. Tian, J. Wang, B. Zhang, P. Chu, S. Li, M. M. Pathan, Y. Deng, and S. Wu,“Evaluation of five diffeomorphic image registration algorithms for mouse brain magnetic resonance microscopy,” J. Microsc,268(2),2017, pp. 141-154.
[11] 付振荣,林岚,张柏雯,等.基于MRM的小鼠脑模板创建的研究进展[J].中国医疗设备,2016, 31(2): 25-30。
[12] A. Gholipour, A. Akhondiasl, J. A. Estroff and S. K. Warfield, “Multi-atlas multi-shape segmentation of fetal brain MRI for volumetric and morphometric analysis of ventriculomegaly,”J. Neuroimage, 2012 15; 60(3): 1819-31.
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Cite This Article
  • APA Style

    Lin Lan, Wang Jingxuan, Fu Zhenrong, Wu Xuetao, Gu Kenan, et al. (2017). Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools. Science Discovery, 5(6), 486-491. https://doi.org/10.11648/j.sd.20170506.26

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    ACS Style

    Lin Lan; Wang Jingxuan; Fu Zhenrong; Wu Xuetao; Gu Kenan, et al. Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools. Sci. Discov. 2017, 5(6), 486-491. doi: 10.11648/j.sd.20170506.26

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    AMA Style

    Lin Lan, Wang Jingxuan, Fu Zhenrong, Wu Xuetao, Gu Kenan, et al. Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools. Sci Discov. 2017;5(6):486-491. doi: 10.11648/j.sd.20170506.26

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  • @article{10.11648/j.sd.20170506.26,
      author = {Lin Lan and Wang Jingxuan and Fu Zhenrong and Wu Xuetao and Gu Kenan and Wu Shuicai},
      title = {Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools},
      journal = {Science Discovery},
      volume = {5},
      number = {6},
      pages = {486-491},
      doi = {10.11648/j.sd.20170506.26},
      url = {https://doi.org/10.11648/j.sd.20170506.26},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170506.26},
      abstract = {In recent years, with the deepening of brain science research, mouse magnetic resonance microscopy (MRM) for neuroimaging research has gradually become a main research interest. Brain segmentation is an essential techniques for investigating the brain morphometry, while the traditional approach to segment a given brain involves the manual delineation of the ROIs by an expert. This practice can be slow and unscalable. Although automatic atlas-based segmentation approaches have been developed and validated for the human brain MRI, there is limited work for the mouse brain MRM. This paper combined optimized image registration and multi-atlas model for mouse brain segmentation. The results showed that multiple atlases with optimized geodesic-SyN can best improve the segmentation accuracy in the mouse brain, and registration algorithm plays important role in performance improvement.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools
    AU  - Lin Lan
    AU  - Wang Jingxuan
    AU  - Fu Zhenrong
    AU  - Wu Xuetao
    AU  - Gu Kenan
    AU  - Wu Shuicai
    Y1  - 2017/12/28
    PY  - 2017
    N1  - https://doi.org/10.11648/j.sd.20170506.26
    DO  - 10.11648/j.sd.20170506.26
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 486
    EP  - 491
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20170506.26
    AB  - In recent years, with the deepening of brain science research, mouse magnetic resonance microscopy (MRM) for neuroimaging research has gradually become a main research interest. Brain segmentation is an essential techniques for investigating the brain morphometry, while the traditional approach to segment a given brain involves the manual delineation of the ROIs by an expert. This practice can be slow and unscalable. Although automatic atlas-based segmentation approaches have been developed and validated for the human brain MRI, there is limited work for the mouse brain MRM. This paper combined optimized image registration and multi-atlas model for mouse brain segmentation. The results showed that multiple atlases with optimized geodesic-SyN can best improve the segmentation accuracy in the mouse brain, and registration algorithm plays important role in performance improvement.
    VL  - 5
    IS  - 6
    ER  - 

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Author Information
  • College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

  • College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

  • College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

  • College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

  • College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

  • College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

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