Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools
Science Discovery
Volume 5, Issue 6, November 2017, Pages: 486-491
Received: Dec. 27, 2017; Published: Dec. 28, 2017
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Authors
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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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.
Keywords
Mouse, Magnetic Resource Microscopy, Brain Segmentation
To cite this article
Lin Lan, Wang Jingxuan, Fu Zhenrong, Wu Xuetao, Gu Kenan, Wu Shuicai, Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools, Science Discovery. Vol. 5, No. 6, 2017, pp. 486-491. doi: 10.11648/j.sd.20170506.26
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