TY - GEN
T1 - Predicting cortical 3-hinge locations via structural connective features
AU - Li, Xiao
AU - Zhang, Tuo
AU - Dong, Qinglin
AU - Zhang, Shu
AU - Hu, Xintao
AU - Du, Lei
AU - Guo, Lei
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/15
Y1 - 2017/6/15
N2 - Cortical folds encode crucial information of brain development, cytoarchitecture and function. It is widely accepted that common anatomy is preserved across individuals within species, while huge variation still hamper establishing fine-grained anatomical correspondences and predicting the locations of a specific anatomical pattern via conventional image registration methods, especially for complex cortical folding pattern, such as gyral 3-hinge. Recently, white matter axonal wiring patterns have been suggested to be strongly correlative to cortical folding patterns. Therefore, in this work, we studied the relation between complex 3-hinge folding patterns and structural connective patterns, and proposed effective methods to predict the locations of 3-hinges by using structural connective features and spatial distribution patterns. The prediction accuracy of our methods outperforms conventional image registration methods.
AB - Cortical folds encode crucial information of brain development, cytoarchitecture and function. It is widely accepted that common anatomy is preserved across individuals within species, while huge variation still hamper establishing fine-grained anatomical correspondences and predicting the locations of a specific anatomical pattern via conventional image registration methods, especially for complex cortical folding pattern, such as gyral 3-hinge. Recently, white matter axonal wiring patterns have been suggested to be strongly correlative to cortical folding patterns. Therefore, in this work, we studied the relation between complex 3-hinge folding patterns and structural connective patterns, and proposed effective methods to predict the locations of 3-hinges by using structural connective features and spatial distribution patterns. The prediction accuracy of our methods outperforms conventional image registration methods.
KW - 3-hinge
KW - Connective features
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85023201610&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2017.7950577
DO - 10.1109/ISBI.2017.7950577
M3 - 会议稿件
AN - SCOPUS:85023201610
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 533
EP - 537
BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PB - IEEE Computer Society
T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Y2 - 18 April 2017 through 21 April 2017
ER -