TY - GEN
T1 - A Novel Two-Stage Multi-view Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship Between Brain Function and Structure
AU - Zhang, Shu
AU - Kang, Yanqing
AU - Yu, Sigang
AU - Wu, Jinru
AU - Shi, Enze
AU - Wang, Ruoyang
AU - He, Zhibin
AU - Du, Lei
AU - Zhang, Tuo
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Understanding the relationship between brain function and structure is vital important in the field of brain image analysis. It elucidates the working mechanism of the brain, which will contribute to better understand the brain and simulate the brain-like system. Extensive efforts have been made on this topic, but still far from the satisfactory. The major difficulties are at least two aspects. One is the huge individual difference among the subjects, which makes it hard to obtain stable results at groupwise level, e.g., noise signals can significantly affect the exploring process. The other one is the huge difference between functional and structural features of the brain, both in their pattern and size, which are very different. To alleviate the above problems, in this paper, we propose a two-stage multi-view low-rank sparse subspace clustering (Two-stage MLRSSC) method to jointly study the relationship between brain function and structure and identify the common regions of brain function and structure. The major innovation of proposed Two-stage MLRSSC is that comparable features of brain function and structure can be effectively extracted from low-rank sparse representation, and results are further improved the stability by two-stage strategy. Finally, groupwise-based stable functional and structural common regions are identified for better understanding the relationship. Experimental results shed new ways to explore the brain function and structure, new insights are observed and discussed.
AB - Understanding the relationship between brain function and structure is vital important in the field of brain image analysis. It elucidates the working mechanism of the brain, which will contribute to better understand the brain and simulate the brain-like system. Extensive efforts have been made on this topic, but still far from the satisfactory. The major difficulties are at least two aspects. One is the huge individual difference among the subjects, which makes it hard to obtain stable results at groupwise level, e.g., noise signals can significantly affect the exploring process. The other one is the huge difference between functional and structural features of the brain, both in their pattern and size, which are very different. To alleviate the above problems, in this paper, we propose a two-stage multi-view low-rank sparse subspace clustering (Two-stage MLRSSC) method to jointly study the relationship between brain function and structure and identify the common regions of brain function and structure. The major innovation of proposed Two-stage MLRSSC is that comparable features of brain function and structure can be effectively extracted from low-rank sparse representation, and results are further improved the stability by two-stage strategy. Finally, groupwise-based stable functional and structural common regions are identified for better understanding the relationship. Experimental results shed new ways to explore the brain function and structure, new insights are observed and discussed.
KW - Brain function
KW - Brain structure
KW - Low-Rank Sparse Subspace Clustering
KW - Multi-View
UR - http://www.scopus.com/inward/record.url?scp=85144829065&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21014-3_20
DO - 10.1007/978-3-031-21014-3_20
M3 - 会议稿件
AN - SCOPUS:85144829065
SN - 9783031210136
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 191
EP - 200
BT - Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Lian, Chunfeng
A2 - Cao, Xiaohuan
A2 - Rekik, Islem
A2 - Xu, Xuanang
A2 - Cui, Zhiming
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 18 September 2022
ER -