TY - GEN
T1 - Automatic parcellation of cortical surfaces using random forests
AU - Meng, Yu
AU - Li, Gang
AU - Gao, Yaozong
AU - Shen, Dinggang
PY - 2015/7/21
Y1 - 2015/7/21
N2 - 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 of 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.
AB - 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 of 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.
KW - Cortical surface parcellation
KW - Haar-like features
KW - context feature
KW - graph cuts
KW - random forests
UR - http://www.scopus.com/inward/record.url?scp=84944323287&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944323287&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7163995
DO - 10.1109/ISBI.2015.7163995
M3 - Conference contribution
AN - SCOPUS:84944323287
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 810
EP - 813
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -