TY - GEN
T1 - Group-wise graph matching of cortical gyral hinges
AU - Zhang, Tuo
AU - Li, Xiao
AU - Zhao, Lin
AU - Huang, Ying
AU - He, Zhibin
AU - Guo, Lei
AU - Liu, Tianming
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Human brain image alignment has long been an intriguing research topic. The difficulty lies in the huge inter-individual variation. Also, it is not fully understood how structural similarity across subjects is related to functional correspondence. Recently, a gyral folding pattern, which is the conjunction of gyri from multiple directions and termed gyral hinge, was characterized. Gyral hinges have been demonstrated to have structural and functional importance and some of them were found to have cross-subject correspondences by manual labeling. However, there is no automatic method to estimate the cross-subject correspondences for whole-brain gyral hinges yet. To this end, we propose a novel group-wise graph matching framework, to which we feed structural connective matrices among gyral hinges from all subjects. The correspondence estimated by this framework is demonstrated by cross-subject consistency of both structural connective and functional profiles. Also, our results outperform the correspondences identified by pairwise graph matching and image-based registration methods.
AB - Human brain image alignment has long been an intriguing research topic. The difficulty lies in the huge inter-individual variation. Also, it is not fully understood how structural similarity across subjects is related to functional correspondence. Recently, a gyral folding pattern, which is the conjunction of gyri from multiple directions and termed gyral hinge, was characterized. Gyral hinges have been demonstrated to have structural and functional importance and some of them were found to have cross-subject correspondences by manual labeling. However, there is no automatic method to estimate the cross-subject correspondences for whole-brain gyral hinges yet. To this end, we propose a novel group-wise graph matching framework, to which we feed structural connective matrices among gyral hinges from all subjects. The correspondence estimated by this framework is demonstrated by cross-subject consistency of both structural connective and functional profiles. Also, our results outperform the correspondences identified by pairwise graph matching and image-based registration methods.
KW - Cross-subject correspondence
KW - Gyral hinges
KW - Structural connectivity
UR - https://www.scopus.com/pages/publications/85075656634
UR - https://www.scopus.com/pages/publications/85075656634#tab=citedBy
U2 - 10.1007/978-3-030-32251-9_9
DO - 10.1007/978-3-030-32251-9_9
M3 - Conference contribution
AN - SCOPUS:85075656634
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 75
EP - 83
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -