Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools
Volume 5, Issue 6, November 2017, Pages: 486-491
Received: Dec. 27, 2017;
Published: Dec. 28, 2017
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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
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.
Multi-Atlas Segmentation of Mouse Brain MRM Based on Optimized Advanced Normalization Tools, Science Discovery.
Vol. 5, No. 6,
2017, pp. 486-491.
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.
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.
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.
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.
H. BENVENISTE, and S. BLACKBAND, “MR microscopy and highresolution small animal MRI: Applications in neuroscienceresearch,” J. Prog Neurobiol, 67(5), 2002, pp. 393-420.
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.
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.
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.
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.
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。
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.
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.
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.
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.
K. B. J. Franklin, G. Paxinos, The Mouse Brain: In Stereotaxic Coordinates, 1st ed., New York: Academic Press, 2001.