Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Symposium on Biomedical Imaging |
| Publisher | IEEE Computer Society |
| Pages | 810-813 |
| Number of pages | 4 |
| Volume | 2015-July |
| ISBN (Print) | 9781479923748 |
| DOIs | |
| Publication status | Published - 2015 Jul 21 |
| Externally published | Yes |
| Event | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: 2015 Apr 16 → 2015 Apr 19 |
Other
| Other | 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 |
|---|---|
| Country | United States |
| City | Brooklyn |
| Period | 15/4/16 → 15/4/19 |
Fingerprint
Keywords
- context feature
- Cortical surface parcellation
- graph cuts
- Haar-like features
- random forests
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Automatic parcellation of cortical surfaces using random forests. / Meng, Yu; Li, Gang; Gao, Yaozong; Shen, Dinggang.
Proceedings - International Symposium on Biomedical Imaging. Vol. 2015-July IEEE Computer Society, 2015. p. 810-813 7163995.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 - context feature
KW - Cortical surface parcellation
KW - graph cuts
KW - Haar-like features
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
SN - 9781479923748
VL - 2015-July
SP - 810
EP - 813
BT - Proceedings - International Symposium on Biomedical Imaging
PB - IEEE Computer Society
ER -