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Nonlocal atlas-guided multi-channel forest learning for human brain labeling.
Ma G, Gao Y, Wu G, Wu L, Shen D. Ma G, et al. Med Phys. 2016 Feb;43(2):1003-19. doi: 10.1118/1.4940399. Med Phys. 2016. PMID: 26843260 Free PMC article.
PURPOSE: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical …
PURPOSE: It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. …
Non-local Atlas-guided Multi-channel Forest Learning for Human Brain Labeling.
Ma G, Gao Y, Wu G, Wu L, Shen D. Ma G, et al. Med Image Comput Comput Assist Interv. 2015 Oct;9351:719-726. doi: 10.1007/978-3-319-24574-4_86. Epub 2015 Nov 18. Med Image Comput Comput Assist Interv. 2015. PMID: 26942235 Free PMC article.
Labeling MR brain images into anatomically meaningful regions is important in many quantitative brain researches. ...In particular, we employ a multi-channel random forest to learn the nonlinear relationship between these hybrid fe …
Labeling MR brain images into anatomically meaningful regions is important in many quantitative brain researches. ...In …