Automatic parcellation of cortical surfaces using random forests

Yu Meng, Gang Li, Yaozong Gao, Dinggang Shen
2015 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)  
Automatic and accurate parcellation of cortical surfaces into anatomically and functionally meaningful regions is of fundamental importance in brain mapping. In this paper, we propose a new method leveraging random forests and graph cuts methods to parcellate cortical surfaces into a set of gyral-based regions, using multiple surface atlases with manual labels by experts. Specifically, our method first takes advantage of random forests and auto-context methods to learn the optimal utilization
more » ... cortical features for rough parcellation and then the graph cuts method to further refine the parcellation for improved accuracy and spatial consistency. Particularly, to capitalize on random forests, we propose a novel definition of Haar-like features on cortical surfaces based on spherical mapping. The proposed method has been validated on cortical surfaces from 39 adult brain MR images, each with 35 regions manually labeled by a neuroanatomist, achieving the average Dice ratio of 0.902, higher than the-state-of-art methods.
doi:10.1109/isbi.2015.7163995 pmid:26405505 pmcid:PMC4578305 dblp:conf/isbi/MengLGS15 fatcat:vfs6aana45bghjiusevjx6gozi