TY - GEN
T1 - Subject-specific estimation of missing cortical thickness maps in developing infant brains
AU - Meng, Yu
AU - Li, Gang
AU - Gao, Yaozong
AU - Gilmore, John H.
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2016
Y1 - 2016
N2 - To accurately chart the dynamic brain developmental trajectories in infants, many longitudinal neuroimaging studies prefer having a complete dataset. Unfortunately, missing data at certain time points are unavoidable in longitudinal datasets. To better use incomplete longitudinal data, we propose a novel method to estimate the subject-specific vertex-wise cortical thickness maps at missing time points, by using a customized regression forest, Dynamically-Assembled Regression Forest (DARF). DARF ensures spatial smoothness of the estimated cortical thickness maps and also the computational efficiency. The proposed method can fully exploit the available information from the subjects both with and without missing scans. Our method has been applied to estimate the missing cortical thickness maps in a longitudinal infant dataset, which includes 31 healthy subjects, with each having up to 5 scans. The experimental results indicate that our method can accurately estimate missing cortical thickness maps, with the average vertex-wise error less than 0.23 mm.
AB - To accurately chart the dynamic brain developmental trajectories in infants, many longitudinal neuroimaging studies prefer having a complete dataset. Unfortunately, missing data at certain time points are unavoidable in longitudinal datasets. To better use incomplete longitudinal data, we propose a novel method to estimate the subject-specific vertex-wise cortical thickness maps at missing time points, by using a customized regression forest, Dynamically-Assembled Regression Forest (DARF). DARF ensures spatial smoothness of the estimated cortical thickness maps and also the computational efficiency. The proposed method can fully exploit the available information from the subjects both with and without missing scans. Our method has been applied to estimate the missing cortical thickness maps in a longitudinal infant dataset, which includes 31 healthy subjects, with each having up to 5 scans. The experimental results indicate that our method can accurately estimate missing cortical thickness maps, with the average vertex-wise error less than 0.23 mm.
KW - Infant brain development
KW - Longitudinal cortical thickness
KW - Missing data completion
UR - http://www.scopus.com/inward/record.url?scp=84981303106&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84981303106&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42016-5_8
DO - 10.1007/978-3-319-42016-5_8
M3 - Conference contribution
AN - SCOPUS:84981303106
SN - 9783319420158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 92
BT - Medical Computer Vision
A2 - Kelm, Michael
A2 - Müller, Henning
A2 - Menze, Bjoern
A2 - Zhang, Shaoting
A2 - Metaxas, Dimitris
A2 - Langs, Georg
A2 - Montillo, Albert
A2 - Cai, Weidong
PB - Springer Verlag
T2 - International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Y2 - 9 October 2015 through 9 October 2015
ER -