TY - GEN
T1 - MCI identification by joint learning on multiple MRI data
AU - Gao, Yue
AU - Wee, Chong Yaw
AU - Kim, Minjeong
AU - Giannakopoulos, Panteleimon
AU - Montandon, Marie Louise
AU - Haller, Sven
AU - Shen, Dinggang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - The identification of subtle brain changes that are associated with mild cognitive impairment (MCI), the at-risk stage of Alzheimer’s disease, is still a challenging task. Different from existing works, which employ multimodal data (e.g., MRI, PET or CSF) to identify MCI subjects from normal elderly controls, we use four MRI sequences, including T1-weighted MRI (T1), Diffusion Tensor Imaging (DTI), Resting-State functional MRI (RS-fMRI) and Arterial Spin Labeling (ASL) perfusion imaging. Since these MRI sequences simultaneously capture various aspects of brain structure and function during clinical routine scan, it simplifies finding the relationship between subjects by incorporating the mutual information among them. To this end, we devise a hypergraph-based semisupervised learning algorithm. In particular, we first construct a hypergraph for each of MRI sequences separately using a star expansion method with both the training and testing data. A centralized learning is then performed to model the optimal relevance between subjects by incorporating mutual information between different MRI sequences. We then combine all centralized hypergraphs by learning the optimal weight of each hypergraph based on the minimum Laplacian. We apply our proposed method on a cohort of 41 consecutive MCI subjects and 63 age-and-gender matched controls with four MRI sequences. Our method achieves at least a 7.61% improvement in classification accuracy compared to state-of-theart methods using multiple MRI data.
AB - The identification of subtle brain changes that are associated with mild cognitive impairment (MCI), the at-risk stage of Alzheimer’s disease, is still a challenging task. Different from existing works, which employ multimodal data (e.g., MRI, PET or CSF) to identify MCI subjects from normal elderly controls, we use four MRI sequences, including T1-weighted MRI (T1), Diffusion Tensor Imaging (DTI), Resting-State functional MRI (RS-fMRI) and Arterial Spin Labeling (ASL) perfusion imaging. Since these MRI sequences simultaneously capture various aspects of brain structure and function during clinical routine scan, it simplifies finding the relationship between subjects by incorporating the mutual information among them. To this end, we devise a hypergraph-based semisupervised learning algorithm. In particular, we first construct a hypergraph for each of MRI sequences separately using a star expansion method with both the training and testing data. A centralized learning is then performed to model the optimal relevance between subjects by incorporating mutual information between different MRI sequences. We then combine all centralized hypergraphs by learning the optimal weight of each hypergraph based on the minimum Laplacian. We apply our proposed method on a cohort of 41 consecutive MCI subjects and 63 age-and-gender matched controls with four MRI sequences. Our method achieves at least a 7.61% improvement in classification accuracy compared to state-of-theart methods using multiple MRI data.
UR - http://www.scopus.com/inward/record.url?scp=84951147140&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24571-3_10
DO - 10.1007/978-3-319-24571-3_10
M3 - Conference contribution
AN - SCOPUS:84951147140
SN - 9783319245706
SN - 9783319245706
SN - 9783319245706
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 85
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
A2 - Hornegger, Joachim
A2 - Frangi, Alejandro F.
A2 - Wells, William M.
A2 - Frangi, Alejandro F.
A2 - Navab, Nassir
A2 - Hornegger, Joachim
A2 - Navab, Nassir
A2 - Wells, William M.
A2 - Wells, William M.
A2 - Frangi, Alejandro F.
A2 - Hornegger, Joachim
A2 - Navab, Nassir
PB - Springer Verlag
T2 - 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 9 October 2015
ER -