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

Lin Lan; Wang Jingxuan; Fu Zhenrong; Wu Xuetao; Gu Kenan; Wu Shuicai

doi:doi:10.11648/j.sd.20170506.26

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

Lin Lan, Wang Jingxuan, Fu Zhenrong, Wu Xuetao, Gu Kenan, Wu Shuicai

Published in Science Discovery (Volume 5, Issue 6)

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.
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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

Lin Lan

College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
Wang Jingxuan

College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
Fu Zhenrong

College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
Wu Xuetao

College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
Gu Kenan

College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
Wu Shuicai

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

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Plain Text BibTeX RIS

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

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://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}
}

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