TY - GEN
T1 - Longitudinal Sparse Regression for Neuroimage Based Consciousness Assessing and Tracking of Hydrocephalus Patients
AU - Chen, Sen
AU - Tang, Weijun
AU - Hu, Jin
AU - Cheng, Yawang
AU - Wang, Qian
AU - Wu, Xuehai
AU - Shen, Dinggang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/25
Y1 - 2018/5/25
N2 - Hydrocephalus is a condition that causes ventricle enlargement by pathological accumulation of cerebrospinal fluid (CSF) and ends with different levels of disorders of consciousness (DOCs). Assessment of the consciousness level will help for the planning of the treatment of the patients. In this paper, a longitudinal sparse regression model featured by the temporal constraint is proposed to assess the levels of consciousness for hydrocephalus patients and to track their temporal alterations based on magnetic resonance (MR) images. Specifically, for the time points before and after neurosurgeries, we extract features from the corresponding MR scans and then regress out the clinical scores that reflect the respective consciousness levels. The longitudinal regression model can thus be applied to automatically track and evaluate the consciousness level change for individual patients, while the reading of the regression can act as an important indicator for the planning of subsequent treatment in clinical practice.
AB - Hydrocephalus is a condition that causes ventricle enlargement by pathological accumulation of cerebrospinal fluid (CSF) and ends with different levels of disorders of consciousness (DOCs). Assessment of the consciousness level will help for the planning of the treatment of the patients. In this paper, a longitudinal sparse regression model featured by the temporal constraint is proposed to assess the levels of consciousness for hydrocephalus patients and to track their temporal alterations based on magnetic resonance (MR) images. Specifically, for the time points before and after neurosurgeries, we extract features from the corresponding MR scans and then regress out the clinical scores that reflect the respective consciousness levels. The longitudinal regression model can thus be applied to automatically track and evaluate the consciousness level change for individual patients, while the reading of the regression can act as an important indicator for the planning of subsequent treatment in clinical practice.
KW - disorder of consciousness
KW - feature selection
KW - hydrocephalus
KW - longitudinal sparse regression
UR - http://www.scopus.com/inward/record.url?scp=85048474788&partnerID=8YFLogxK
U2 - 10.1109/BigComp.2018.00103
DO - 10.1109/BigComp.2018.00103
M3 - Conference contribution
AN - SCOPUS:85048474788
T3 - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
SP - 595
EP - 598
BT - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
Y2 - 15 January 2018 through 18 January 2018
ER -