Science Discovery

| Peer-Reviewed |

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

Received: 27 December 2017    Accepted:     Published: 28 December 2017
Views:       Downloads:

Share This Article

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.

DOI 10.11648/j.sd.20170506.26
Published in Science Discovery (Volume 5, Issue 6, November 2017)
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), 2024. Published by Science Publishing Group

Keywords

Mouse, Magnetic Resource Microscopy, Brain Segmentation

References
[1] L. Lin, Z. Fu, X. Xu, and S. Wu, “Mouse brain magnetic resonance microscopy: Applications in Alzheimer disease,”J. Microscopy Research & Technique, 78(5)2015, pp. 416-424.
[2] S. E. Wang, and C. H. Wu, “Physiological and Histological Evaluations of the Cochlea between 3xTg-AD Mouse Model of Alzheimer's Diseases and R6/2 Mouse Model of Huntington's Diseases,” J. Chinese Journal of Physiology, 58(6), 2015, pp. 359.
[3] M. Verma, D. Beaulieu-Abdelahad, G. Ait-Ghezala, R. Li, F. Crawford, M. Mullan, and D. Paris, “Chronic Anatabine Treatment Reduces Alzheimer’s Disease (AD)-Like Pathology and Improves Socio-Behavioral Deficits in a Transgenic Mouse Model of AD,” J. PLOS ONE, 2015, pp. 10.
[4] M. Venissa, Z. Tanja, A. Abdelraheim, and S. Björn “Microglia-Mediated Neuroinflammation and Neurotrophic Factor-Induced Protection in the MPTP Mouse Model of Parkinson’s Disease-Lessons from Transgenic Mice,” J. International Journal of Molecular Sciences, 17(2), 2016, pp. 151.
[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。
[6] H. BENVENISTE, and S. BLACKBAND, “MR microscopy and highresolution small animal MRI: Applications in neuroscienceresearch,” J. Prog Neurobiol, 67(5), 2002, pp. 393-420.
[7] B. DRIEHUYS, J. NOULS, A. BADEA, E. Bucholz, K. Ghaghada, A. Petiet, and L. W. Hedlund, “Small animal imaging withmagnetic resonance microscopy,” J. ILAR J, 49(1), 2008, pp. 35-53.
[8] B. B. Avants, C. L. Epstein, M Grossman, and J. C. Gee, “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain,” J. Medical Image Analysis, 12(1), 2008, pp. 26-41.
[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.
[13] J. E. Iglesias and M. R. Sabuncu,“Multi-atlas segmentation of biomedical images: A survey,”J. Med Image Anal,24(1), pp. 205-219, Aug 2015.
[14] L. Lin, S. Wu, G. Bin, and C. Yang,“Intensity Inhomogeneity Correction Using N3 on Mouse Brain,”J. Magnetic Resonance Microscopy [J]. Journal of Neuroimaging Official Journal of the American Society of Neuroimaging, 23(4),2013, pp. 502-507.
[15] J. G. Sled, A. P. Zijdenbos, and A. C. Evans, “A nonparametric method for automatic correction of intensity nonuniformity in MRI data,” J. IEEE Transactions on Medical Imaging, 17(1), 1998, pp. 87-97.
[16] K. B. J. Franklin, G. Paxinos, The Mouse Brain: In Stereotaxic Coordinates, 1st ed., New York: Academic Press, 2001.
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

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

    Copy | Download

    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

    Copy | Download

    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

    Copy | Download

  • @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://download.sciencepg.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}
    }
    

    Copy | Download

  • 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  - 

    Copy | Download

  • Sections